Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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Citation - Scopus: 1
Design and Fpga Implementation of Uav Simulator for Fast Prototyping
(IEEE, 2023) Aydın, Yusuf; Ayhan, Tuba; Akyavaş , İrfan
As production and advances in motor and battery cell technology progress, unmanned aerial vehicles (UAVs) are gaining more and more acceptance and popularity. Unfortunately, the design and prototyping of UAVs is an expensive and long process. This paper proposes a fast, component based simulation environment for UAVs so that they can be roughly tested without a damage risk. Moreover, the combined effect of individual component choices can be observed with the simulator to reduce design time. The simulator is flexible in the sense that detailed aerodynamic effects and selected components models can be included. In this work, the simulator is proposed, model parameters are extracted for a particular UAV for testing the simulator and it is implemented on an field programmable gate array (FPGA) to increase simulation speed. The simulator calculates battery state of charge (SOC), position, velocity and acceleration of the UAV with gravity, drag, propeller air inflow velocity. The simulator runs on the FPGA fabric of AMD-XCKU13P with simulation steps of 1 ms.
Citation - Scopus: 5
An Antipodal Vivaldi Antenna Design for Torso Imaging in a Coupling Medium
(IEEE, 2021) Çayören, Mehmet; Bilgin, Egemen; Joof, Sulayman; Doğu, Semih
An antipodal Vivaldi antenna designed to operate in a coupling medium with a relative dielectric constant of epsilon(r) = 25 for microwave imaging of torso is presented in this paper. The proposed antenna is similar to the conventional antipodal Vivaldi antenna but with optimized parameters to radiate in the desired coupling medium. The antenna has a size of 120x70 mm(2) and operating over 230-1000 MHz frequency bandwidth with a peak gain of 5.42 dBi and peak front-to-back ratio of 143 dB. The designed antenna shows a better performance compared to other antennas used for microwave torso imaging. To assess the actual performance, a realistic human torso phantom is implemented to detect the water accumulation in the lungs, and as the inversion method linear sampling method is used. The 3-D reconstruction results show that the proposed antenna can be a candidate for microwave torso imaging applications.
Diyalog Geliştirme için Bağlaşımlı Tensör Ayrıştırma Yöntemleri
(TÜBİTAK, 2021) Şimşek, Serap Kırbız; Cemgil, Ali Taylan; Liutkus, Antoine
Ayrıştırma tabanlı ses modelleme yöntemleri, hesaplama gücünün artmasıyla ve istatistiksel modelleme yöntemlerinin gelişmesiyle birlikte yaygın olarak kullanılmaktadır. Bu yöntemler, ses kodlama, müziksel bilgi çıkarımı, müziğin notaya dökülmesi, içerik analizi, kaynak ayrıştırma, ses onarımı ve gürbüz konuşmacı tanımanın da aralarında bulunduğu birçok alanda kullanılmaktadır. Bizim bu projede temel amacımız, birden fazla kaynak içeren ses kayıtlarındaki konuşma işaretlerini güçlendirmek için kaynak ayrıştırma algoritmalarından faydalanarak bir yöntem geliştirmektir. Diyalog ve ortamdaki diğer sesler arasındaki doğru dengeyi bulmak ses mühendisleri için önemli bir problem olup, dinleyici şikayetlerinin de gittikçe artan bir sebebini oluşturmaktadır. Dinleyiciler, kendi kişisel tercihlerine, dinleme ortamlarına ve duymalarına uygun olarak diyalog ve çevresel sesler arasındaki ses dengesini kendileri ayarlamak istemektedirler. Bu projedeki temel amaçlar ve aşamalar aşağıdaki gibidir: i) Durağan olmayan çok boyutlu zaman serilerinde, matris ve tensör ayrıştırma modellerini kullanarak diyalog içeren ses kayıtlarından diyalogların ayrıştırılması ve bunun daha sonra kayıtta bulunan diğer seslerle farklı oranlarda yeniden birleştirilmesiyle, kullanıcının ihtiyaçlarına ya da zevkine dayalı bir kayıt dinlemesini sağlama ii) Televizyon programları gibi akan veri üzerinde de çalışabilmek üzere, önerilen yöntemin gerçek zamanda çalışması. Bu bağlamda, veri geldikçe gerçek zamanlı olarak işlenecektir. iii) Geliştirilen yöntemlerin etkinliğinin gerçek uygulamalarda kullanımı. Projenin çıktıları olan modelleme, çıkarım ve model seçimi yöntemleri; işaret işleme, yapay öğrenme ve istatistik alanlarında temel metodolojik katkılar yapmaktatır. Bunun dışında çıktılar, bilgi madenciliği, biyoinformatik, sistem biyolojisi, yer bilimleri, karmaşık sistemler, algılayıcı ağları, finans veya akustik konularındaki büyük veri öbeklerinin incelendiği çalışmaları destekleyecektir. Bu bağlamda, MEF Üniversitesi bünyesinde yerli ve uluslararası alanda süren işbirliklerinin sürdürülmesi ve geliştirilmesi de planlanmaktadır.
Citation - WoS: 3
Citation - Scopus: 5
Developing an Automatic Transcription and Retrieval System for Spoken Lectures in Turkish
(2017) Arısoy, Ebru
With the increase of online video lectures, using speech and language processing technologies for education has become quite important. This paper presents an automatic transcription and retrieval system developed for processing spoken lectures in Turkish. The main steps in the system are automatic transcription of Turkish video lectures using a large vocabulary continuous speech recognition (LVCSR) system and finding keywords on the lattices obtained from the LVCSR system using a speech retrieval system based on keyword search. While developing this system, first a state-of-the-art LVCSR system was developed for Turkish using advance acoustic modeling methods, then keywords were extracted automatically front word sequences in the reference transcriptions of video lectures, and a speech retrieval system was developed for searching these keywords in the lattice output of the LVCSR system. The spoken lecture processing system yields 14.2% word error rate and 0.86 maximum term weighted value on the test data.
Citation - Scopus: 1
Solving Xor In Spike Neural Network (SNN) With Component-off-the-shelf
(Institute of Electrical and Electronics Engineers Inc., 2024) Cikikci, S.V.; Orek, E.; Ozgen, A.K.; Yavuz, A.; Ayhan, Tuğba
This paper addresses the solution of the XOR problem with Spiking Neural Networks (SNN) in order to improve energy efficiency and computational performance as Moore's Law approaches its limits. SNN is capable of solving nonlinear problems while saving energy by mimicking the working principles of biological neurons. For this purpose, a SNN consisting of 12 neurons was implemented on a breadboard using the Leaky Integrate and Fire (LIF) model. In the input layer of the network, 50 Hz and 100 Hz signals are processed with frequency sensitive filters. With the help of bandpass and low-pass filters, additive and inverting operational amplifiers, the XOR problem is successfully solved. © 2024 IEEE.
Citation - WoS: 11
Citation - Scopus: 11
Resolving Conflicts During Human-Robot Co-Manipulation
(IEEE Computer Society, 2023) Al-Saadi, Zaid; Hamad, Yahya M.; Aydin, Yusuf; Kucukyilmaz, Ayse; Basdogan, Cagatay
This paper proposes a machine learning ( ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.
A Bayesian Allocation Model Based Approach To Mixed Membership Stochastic Blockmodels
(Taylor and Francis Ltd., 2022) Kırbız, Serap; Hızlı, Çağlar
Although detecting communities in networks has attracted considerable recent attention, estimating the number of communities is still an open problem. In this paper, we propose a model, which replicates the generative process of the mixed-membership stochastic block model (MMSB) within the generic allocation framework of Bayesian allocation model (BAM) and BAM-MMSB. In contrast to traditional blockmodels, BAM-MMSB considers the observations as Poisson counts generated by a base Poisson process and marks according to the generative process of MMSB. Moreover, the optimal number of communities for BAM-MMSB is estimated by computing the variational approximations of the marginal likelihood for each model order. Experiments on synthetic and real data sets show that the proposed approach promises a generalized model selection solution that can choose not only the model size but also the most appropriate decomposition.
Citation - WoS: 23
Citation - Scopus: 23
Experimental Observation of Temperature and Pressure Induced Frequency Fluctuations in Silicon Mems Resonators
(IEEE, 2021) Zhao, Chun; Mustafazade, Arif; Pandit, Milind; Seshia A, Ashwin; Sobreviela, Guillermo; Zou, Xudong
Silicon MEMS resonators are increasingly being adopted for applications in timing and frequency control, as well as precision sensing. It is well established that a key limitation to performance is associated with sensitivity to environmental variables such as temperature and pressure. As a result, technical approaches to address these factors such as vacuum sealing and ovenization of the resonators in a temperature controlled system have been introduced. However, residual sensitivity to such effects can still serve as a significant source of frequency fluctuations and drift in precision devices. This is experimentally demonstrated in this paper for a precision oven-controlled and vacuum-sealed silicon resonators. The frequency fluctuations of oscillators constructed using two separate nearly-identical co-located resonators on the same chip are analysed and differential frequency fluctuations are examined as a means of reducing the impact of common-mode effects such as temperature and pressure. For this configuration, our results show that the mismatch of temperature and pressure coefficients between the resonators ultimately limits the frequency stability.
The Impact of D2d Connections on Network-Assisted Mobile Data Offloading
(IEEE, 2018) Tan, Ahmet Serdar; Zeydan, Engin
The exponential increase of mobile data traffic pushes mobile operators to seek more efficient heterogeneous communication techniques. In this study, multi-user extension methods for multiple attribute decision making algorithms for network-assisted data offloading in heterogeneous wireless networks are developed and performance evaluations are performed in the presence of Device-to-Device (D2D) connections. Evaluations are carried out using simulations to point out the metrics and factors influencing data offloading in heterogeneous networks. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions. Additionally, up to 67% increase in user satisfaction can be achieved when D2D density is kept 68% under a heavy load scenario. The simulation results also indicate the existence of optimal D2D densities in heterogeneous networks depending on the total number of users and available network capacity.
Citation - Scopus: 1
Toward a Novel Neuroscience-Based System Approach Integrating Cognitive and Implicit Learning in Education
(Springer Science and Business Media Deutschland GmbH, 2023) Tsvetkova, Nadezhda; Çakar, Tuna; Veledinskaya, Svetlana; Babanskaya, Olesya; Dorantes-Gonzalez, Dante Jorge
Emotional-enhanced learning is a meaningful driver of engagement leading to long-term memory retention in learners, however, traditional approaches such as problem-based learning, and project-based learning, among others, do not consider brain-based learning guidelines concerning learner’s emotional experience design. The Neuroscience-based Learning (NBL) technique is a novel neuro-educational approach that applies the implicit neuro-physiological mechanisms underlying vivid and highly-arousal emotion-al experiences leading to long-term memory retention. The NBL is devised from a cybernetic system point of view, by explaining the novel neuro-physiological learning scheme describing the relation among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations and reactions, emotions, desires, intentions, higher order cognitive functions, and controlled actions towards the environment. While explaining biological processes, the scheme also relates the types of memory systems with their non-associative and associative learning mechanisms, and the variables that modulate learning. NBL proposes the triggers for a vivid and highly-arousal emotional learning, which are novelty, unpredictability, sense of low control, threat to ego, avoidance (aversion-mediated learning), and reward (reward-based learning). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 1
A Modified Newton Method Formulation for Microwave Imaging
(IEEE, 2020) Coşğun, Sema; Çayören, Mehmet; Bilgin, Egemen; Doğu, Semih
A new variant of Newton type methods has been developed for quantitative microwave imaging. To deal with the ill-posedness of the inverse problems, standard Newton type methods involve a linearization of the so called data equation using the Fréchet derivative with respect to the contrast function. Here, the formulation is expanded to include the object equation, therefore, the formulation seeks to reduce the errors in both the data and the object equations. While this modification does not remove the need to solve forward problem at each step, it nevertheless significantly improves convergence rate and the performance. To assess the efficiency of the proposed technique, numerical simulations with synthetic and experimental data have been carried out. The results demonstrate that the proposed variant outperforms the standard Newton method, and shows comparable performance to the contrast source inversion (CSI) algorithm with fewer iterations.
Citation - Scopus: 1
Mechanical Design of a Haptic Hand Exoskeleton for Tele-Exploration of Explosive Devices
(IEEE, 2023) Dorantes-Gonzalez, Dante Jorge
There are tasks such as remote exploration and manipulation of explosive objects where high dexterity, accuracy, and practicality are necessary. The proposed haptic hand exoskeleton design uses displacement sensors in both flexion-deflection as well as abduction-adduction to replicate the operator's main three fingers' motion and teleoperate a slave robotic hand for disassembling and disposal of explosive objects. The novel design, component selection, and computer-aided design of the haptic virtual prototype were developed and tested.
Citation - Scopus: 1
Validation of the Short Version (tls-15) of the Triangular Love Scale (tls-45) Across 37 Languages (oct, 10.1007/S10508-023-02702-7, 2023)
(Springer/plenum Publishers, 2024) Sorokowski, Piotr; Frederick, David A.; Pisanski, Katarzyna; Kowal, Marta; Dinic, Bojana M.; Sternberg, Robert J.; Gjoneska, Biljana; Demirtaş, Ezgi Toplu
[No Abstract Available]
Zamanla Değişen Iır Alçak Geçiren Filtre ile Dinamik Tartım Yöntemi
(2022) Gülbaş, Mustafa Can; Ayhan, Tuba; Yalçın, Müştak Erhan
Sanayide üretimden sevkiyat aşamasına kadar birçok alanda kullanılan otomatik ağırlık kontrol sistemi, üç adet konveyör bant, en az iki adet fotosel, yük hücresi, işlemci, kullanıcı için kontrol ekranı ve reddedici/yönlendirici kollarından oluşmaktadır. Ağırlık ölçerken, yük hücresi tarafından alınan ölçüm sinyalinin filtrelenmesi aşamasında literatürde kaskat yapıda zamanla değişen alçak geçiren filtrenin önemli bir yeri olduğu gözlemlenmiş ancak sonuca ulaşma konusunda oldukça fazla filtre adedinin mevcut olduğu gözlemlenmiştir. Sonuçlara ulaşma aşamasının hızlandırılması amacıyla bu çalışmada zamanla değişen alçak geçiren filtre ile farklı bir yaklaşım denenmiştir. Sonuca daha hızlı ulaşabilmek için kaskat bağlı alçak geçiren filtre adedi önem arz etmektedir. Filtre adım adım uygulanarak oluşan salınımlardan ürünün ağırlığına minimum filtre adedi ile ulaşmak hedeflenmiştir. Matlab'ta yapılan deneyler sonucunda çok yüksek hızlarda bu işlemle yönetmelikte belirtilen hata limitleri içerisinde sonuçların elde edilemediği görülmüş olup hata limitleri içerisinde elde edilen maksimum hız belirtilmiştir. Sonuç olarak filtre adedi azaltılıp, sönümleme hızlandırılarak oluşan salınımlardan ağırlık verisine yönetmelikte verilen hata sınırları içerisinde ulaşılmıştır.
Citation - WoS: 4
Citation - Scopus: 5
Monitoring of Intracerebral Hemorrhage With a Linear Microwave Imaging Algorithm
(Springer, 2022) Dilman, Ismail; Dogu, Semih; Bilgin, Egemen; Akinci, Mehmet Nuri; Cosgun, Sema; Çayören, Mehmet; Akduman, Ibrahim
Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios. Graphical abstract: [Figure not available: see fulltext.]. © 2022, International Federation for Medical and Biological Engineering.
Citation - Scopus: 1
Foundations of Neuroscience-Based Learning
(Springer International Publishing, 2022) Dorantes-Gonzalez, Dante Jorge
Traditional learning and teaching approaches such as problem-based or project-based learning, among others, do not explicitly consider emotional-enhanced learning, which is a well-known driver of engagement leading to long-term memory retention. On the other hand, existing brain-based learning methods do not provide structured and scientifically-based strategies for the formation of the learner’s emotional experience and engagement. The Neuroscience-based Learning (NBL) technique is a novel neuroeducational approach that explains and applies the implicit neurophysiological mechanisms underlying vivid and highly-arousal emotional experiences leading to long-term memory retention. The NBL is devised from a cybernetics and system approach perspective. It starts from the basis of the neurophysiological learning scheme, describing the relationships among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations, reactions, emotions, desires, intentions, higher-order cognitive functions, and controlled actions to the environment. The scheme relates memory systems, non-associative and associative learning mechanisms, implicit and explicit learning subsystems, signaling chemicals, and their neural subsystems, as well as identifying the amygdala as a key sensor triggering and modulating implicit learning. The NBL method exposes the triggers for vivid and highly arousal emotional learning: novelty, unpredictability, sense of low control, threat to the ego, avoidance (aversion-mediated learning), and reward (reward-based learning) and devises the principles of NBL toward more didactic applications. The foundations for implementing NBL in education and recommendations for learning during the online and pandemic situations were proposed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
Experimental Performance Analysis for Mobile Data Offloading in Heterogeneous Wireless Networks
(2016) Akpolat, Gamze; Zeydan, Engin; Tan, A. Serdar
...
Citation - WoS: 1
Citation - Scopus: 1
İlişkisel Veri Ayrıştırılmasında Model Seçimi
(IEEE, 2019) Kırbız, Serap; Cemgil, Taylan; Hızlı, Çağlar
Abstract—As a fundamental problem in relational data analysis, model selection for relational data factorization is still an open problem. In our work, we propose to estimate model order for mixed membership blockmodels (MMSB) within the generic allocation framework of Bayesian allocation model (BAM). We describe how relational data is represented as Poisson counts of the allocation model, and demonstrate our results both on synthetic and real-world data sets. We believe that the generic allocation perspective promises a generalized model selection solution where we do not only select the model order, but also choose the most appropriate factorization.
Parallelization and Performance Analysis of Reversible Circuit Synthesis
(IEEE, 2018) Susam, Ömercan; Arslan, Şuayb Şefik
Rising popularity of quantum computers in the last decade resulted in increased interest paid to reversible circuitsynthesis process. In this work, a popular essential function-based synthesis algorithm known in the literature is parallelized using openMP library. Contrary to conventional way, essential functions are synthesized when needed without keeping a table-lookup library. When the reversible circuit is synthesized in parallel using a double core processor (4 active threads with hyperthearding technology), around 2.6 speed-up is demonstrated relative tothe performance of serial synthesis work. Comparison between serial and parallel synthesis by using common benchmark circuits demonstrated that the performance of the proposed parallel synthesis is always better in the overall operation work load.
Citation - WoS: 21
Citation - Scopus: 24
An Adaptive Admittance Controller for Collaborative Drilling With a Robot Based on Subtask Classification Via Deep Learning
(Elsevier, 2022) Başdoğan, Çağatay; Niaz, P. Pouya; Aydın, Yusuf; Güler, Berk; Madani, Alireza
In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human–robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.

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