Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

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

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Browsing Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu by browse.metadata.publisher "Institute of Electrical and Electronics Engineers Inc."

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    A Ran/Sdn Controller Based Connectivity Management Platform for Mobile Service Providers
    (Institute of Electrical and Electronics Engineers Inc., 2017) Ayhan, Gökhan; Koca, Melih; Zeydan, Engin; Tan, A. Serdar
    In this demo, we demonstrate the integration of radio access network (RAN)/Software-Defined Networking (SDN) controller with a connectivity management platform designed for mobile wireless networks. This is an architecture designed throughout the EU Celtic-Plus project SIGMONA1. OpenDaylight based RAN/SDN controller and the application server are capable of collecting infrastructure and client related parameters from OpenFlow enabled switches and Android based phones respectively. The decision on the best access network selection is computed at the application server using a Multiple Attribute Decision Making (MADM) algorithm and instructed back to Android-based mobile client for execution of access network selection. © 2017 IFIP.
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    A Resonator Design For Mutual Coupling Reduction Between Microstrip Antennas In Mımo Applications At 28 Ghz
    (Institute of Electrical and Electronics Engineers Inc., 2024) Gollu, A.A.; Polat, B.; Semerci, D.; Bilgin, E.
    A simple resonator structure is proposed to reduce the mutual coupling between rectangular microstrip patch antennas positioned close to each other for MIMO applications at 28 GHz center frequency. Here, the frequency of 28 GHz is chosen because it is one of middle bands for 5G communication in USA. Two microstrip patch antennas with gaps using a common dielectric substrate and a ground plane are employed as antennas and the patches are closely placed with an edge-to-edge distance of 0.6 mm (approximately λ/18). In order to reduce the mutual coupling between these radiating elements and increase the isolation, a resonator is positioned between them and its parameters are optimized. In the simulations, it is observed that the proposed resonator reduces the coupling by approximately 10 dB. By this result, it can be concluded that the proposed structure may be suitable for tightly packed MIMO systems. © 2024 IEEE.
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    Cnn-Based Emotion Recognition Using Data Augmentation and Preprocessing Methods
    (Institute of Electrical and Electronics Engineers Inc., 2023) Toktaş, Tolga; Kırbız, Serap; Kayaoğlu, Bora
    In this paper, a system that recognizes emotion from human faces is designed using Convolutional Neural Networks (CNN). CNN is known to perform well when trained with a large database. The lack of large and balanced publicly available databases that can be used by deep learning methods for emotion recognition is still a challenge. To overcome this problem, the number of data is increased by merging FER+, CK+ and KDEF databases; and preprocessing is applied to the face images in order to reduce the variations in the database. Data augmentation methods are used to reduce the imbalance in the data distribution that still remains despite the increasing number of data in the merged database. The CNN-based method developed using database merging, image preprocessing and data augmentation, achieved emotion recognition with 80% accuracy.
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    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.
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    Uncertainty-Aware Representations for Spoken Question Answering
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ünlü, Merve; Arısoy, Ebru
    This paper describes a spoken question answering system that utilizes the uncertainty in automatic speech recognition (ASR) to mitigate the effect of ASR errors on question answering. Spoken question answering is typically performed by transcribing spoken con-tent with an ASR system and then applying text-based question answering methods to the ASR transcriptions. Question answering on spoken documents is more challenging than question answering on text documents since ASR transcriptions can be erroneous and this degrades the system performance. In this paper, we propose integrating confusion networks with word confidence scores into an end-to-end neural network-based question answering system that works on ASR transcriptions. Integration is performed by generating uncertainty-aware embedding representations from confusion networks. The proposed approach improves F1 score in a question answering task developed for spoken lectures by providing tighter integration of ASR and question answering.