Cilt:62 Sayı:01 (2020)

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https://dspace.ankara.edu.tr/handle/20.500.12575/76532

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Browsing Cilt:62 Sayı:01 (2020) by Author "Bilgisayar Mühendisliği"

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    Generatıng Turkısh Lyrıcs Wıth Long Short Term Memory
    (Ankara Üniversitesi Fen Fakültesi, 2020-06-30) Güzel, Mehmet; Erten, Hakan; Bostancı, Erkan; Bilgisayar Mühendisliği; Mühendislik Fakültesi
    Long Short Term Memory (LSTM) has gained a serious achievement on sequential data which have been used generally videos, text and time-series. In this paper, we aim for generating lyrics with newly created “Turkish Lyrics” dataset. By this time, there have been studies for creating Turkish Lyrics with character-level. Unlike previous studies, we propose to Turkish Lyrics generator working with word-level instead on character-level. Also, for employing LSTM, we can’t send the words as string and words must be vectorized. To vectorize, we tried two ways for encoding the words that are used in dataset and compared them. Firstly, we sample for generating one-hot encoding and then, secondly word-embedding way (Word2Vec). Observational results show us that word- level generation with word-embedding way gives more meaningful and realistic lyrics. Actually, there have not been good results enough to be used for a song because of Turkish Grammar. But, this study encourages authors to work on this field and we do believe that this study will initialize research on this area and lead researchers to contribute to this as well.
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    Learning Dense Contextual Features For Semantic Segmentation
    (Ankara Üniversitesi Fen Fakültesi, 2020-06-30) Keleş, Hacer Yalim; Lim, Long Ang; Bilgisayar Mühendisliği; Mühendislik Fakültesi
    Semantic segmentation, which is one of the key problems in computer vision, has been applied in various application domains such as autonomous driving, robot navigation, or medical imagery, to name a few. Recently, deep learning, especially deep neural networks, have shown significant performance improvement over conventional semantic segmentation methods. In this paper, we present a novel encoder-decoder type deep neural network-based method, namely XSeNet, that can be trained end-to-end in a supervised manner. We adapt ResNet-50 layers as the encoder and design a cascaded decoder that composes of the stack of the X-Modules, which enables the network to learning dense contextual information and having wider field-of-view. We evaluate our method using CamVid dataset, and experimental results reveal that our method can segment most part of the scene accurately and even outperforms previous state-of-the art methods.
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    RF Antenna Desıgn For Button-Type Beam Posıtıon Monıtors Usıng Bıo-Inspıred Optımızatıon Methods
    (Ankara Üniversitesi Fen Fakültesi, 2020-06-30) Aydın, Ayhan; Bostancı, Gazi Erhan; Bilgisayar Mühendisliği; Mühendislik Fakültesi
    Accelerator based facilities are in a leading position for crafting many scientific and technical innovations for a wide range of application from aviation to medicine. Beam Position Monitors (BPMs) are critical diagnostics tools for such facilities. This study presents bio-inspired methods known as Particle Swarm Optimization and Evolutionary Algorithms in order to design RF antennas for button-type BPMs. Our results show that the antenna parameters obtained using this multiple objective approaches present suitable SNR and linearity values for signal processing. It is found that using an antenna radius of 5.5 mm and beam-pipe radius of 17.5 mm, we can obtain SNR values around 40 dB which can be electronically processed.