Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

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

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Citation - WoS: 2
Citation - Scopus: 5
Compositional Neural Network Language Models for Agglutinative Languages
(2016) Saraçlar, Murat; Arısoy, Ebru
Continuous space language models (CSLMs) have been proven to be successful in speech recognition. With proper training of the word embeddings, words that are semantically or syntactically related are expected to be mapped to nearby locations in the continuous space. In agglutinative languages, words are made up of concatenation of stems and suffixes and, as a result, compositional modeling is important. However, when trained on word tokens, CSLMs do not explicitly consider this structure. In this paper, we explore compositional modeling of stems and suffixes in a long short-term memory neural network language model. Our proposed models jointly learn distributed representations for stems and endings (concatenation of suffixes) and predict the probability for stem and ending sequences. Experiments on the Turkish Broadcast news transcription task show that further gains on top of a state-of-theart stem-ending-based n-gram language model can be obtained with the proposed models.
Language Modeling for Turkish Text and Speech Processing
(Springer, 2018) Arısoy, Ebru; Saraçlar, Murat
This chapter presents an overview of language modeling followed by a discussion of the challenges in Turkish language modeling. Sub-lexical units are commonly used to reduce the high out-of-vocabulary (OOV) rates of morphologically rich languages. These units are either obtained by morphological analysis or by unsupervised statistical techniques. For Turkish, the morphological analysis yields word segmentations both at the lexical and surface forms which can be used as sub-lexical language modeling units. Discriminative language models, which outperform generative models for various tasks, allow for easy integration of morphological and syntactic features into language modeling. The chapter provides a review of both generative and discriminative approaches for Turkish language modeling.
Citation - WoS: 3
Citation - Scopus: 5
Single-Slice Microwave Imaging of Breast Cancer by Reverse Time Migration
(Wiley, 2022) Bilgin, Egemen; Cansız, Gökhan; Akduman, İbrahim; Cayoren, Mehmet; Joof, Sulayman; Yılmaz, Tuba
Purpose Microwave imaging of breast cancer is considered and a new microwave imaging prototype including the imaging algorithm, the antenna array, and the measurement configuration is presented. The prototype aims to project the geometrical features of the anomalies inside the breast to a single-slice image at the coronal plane depending on the complex dielectric permittivity variation among the tissues to aid the diagnosis . Methods The imaging prototype uses a solid cylindrical dielectric platform, where a total of 24 optimized Vivaldi antennas are embedded inside to form a uniform circular antenna array. The center of the platform is carved to create a hollow part for placement of the breast and the multistatic, microwave scattering parameters are collected with the antenna array around the hollow center. The dielectric platform further enhances the microwave impedance matching against the breast fat tissue and preserves the vertical polarization during the measurements. In the imaging phase, a computationally efficient inverse electromagnetic scattering method-reverse time migration (RTM)-is considered and adapted in terms of scattering parameters to comply with the actual measurements. Results The prototype system is experimentally tested against tissue-mimicking breast phantoms with realistic dielectric permittivity profiles. The reconstructed single-slice images accurately determined the locations and the geometrical extents of the tumor phantoms. These experiments not only verified the microwave imaging prototype but also provided the first experimental results of the imaging algorithm. Conclusions The presented prototype system implementing the RTM method is capable of reconstructing single-slice, nonanatomical images, where the hotspots correspond to the geometrical projections of the anomalies inside the breast.
The Tuned Mass Damper as a Subject in Engineering Mechanics Dynamics
(IEEE, 2022) Dorantes-Gonzalez, Dante Jorge
The course of Engineering Mechanics Dynamics is one of the most challenging courses for both mechanical and civil engineering programs, among others. But few universities dare to introduce projects to enhance students' curiosity, interest, and engagement toward engineering by constructing do-it-yourself physical prototypes, making measurements, and calculations to compete for the best performance. The intention of this project is to introduce a complex multiple-degree-of-freedom vibration problem in an easy manner, namely, the topic of a tuned mass damper (TMD) applied to earthquake-like oscillations. This type of projects directly addresses all seven student outcomes recommended by the Accreditation Board of Engineering and Technology (ABET). The project develops critical thinking and inquiry skills by designing and constructing the prototype of a building-like structure and its corresponding TMD; conducting an experiment under certain constraints to test the attenuation after an initial displacement; applying an open-source freeware to plot and measure underdamped oscillations; calculating main vibration parameters; as well as comparing performance results with another teams. Students approach this complex tunning problem by trial-and-error of key TMD parameters, a strategy that sparks fun and gambling to the process and competition for the best performance in attenuation efficiency. Data from direct observation of students' performance, student surveys, reports, presentation videos, office hours, and interviews showed that students enthusiastically responded at all project stages, understood the TMD functioning, and appreciated the value of dynamics in engineering in a more meaningful way than it would be without this type of projects.
Citation - WoS: 1
Citation - Scopus: 2
Improving the Usage of Subword-Based Units for Turkish Speech Recognition
(IEEE, 2020) Çetinkaya, Gözde; Saraçlar, Murat; Arısoy, Ebru
Subword units are often utilized to achieve better performance in speech recognition because of the high number of observed words in agglutinative languages. In this study, the proper use of subword units is explored in recognition by a reconsideration of details such as silence modeling and position-dependent phones. A modified lexicon by finite-state transducers is implemented to represent the subword units correctly. Also, we experiment with different types of word boundary markers and achieve the best performance by adding a marker both to the left and right side of a subword unit. In our experiments on a Turkish broadcast news dataset, the subword models do outperform word-based models and naive subword implementations. Results show that using proper subword units leads to a relative word error rate (WER) reductions, which is 2.4%, compared with the word level automatic speech recognition (ASR) system for Turkish.
Citation - WoS: 1
Citation - Scopus: 1
Domain Adaptation Approaches for Acoustic Modeling
(IEEE, 2020) Arısoy, Ebru; Fakhan, Enver
In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data.
Citation - WoS: 41
Citation - Scopus: 49
Adaptive Human Force Scaling Via Admittance Control for Physical Human-Robot Interaction
(IEEE, 2021) Başdoğan, Çağatay; Aydın, Yusuf; Hamad, Yahya M.
The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.
Differential Microwave Imaging of Cerebral Hemorrhage Via Dort Method
(IEEE, 2023) Dilman, İsmail; Bilgin, Egemen; Doğu, Semih
Bleeding in the brain tissues may cause fatal health conditions and continuous monitoring of the change in this blood accumulation becomes important in the first few hours after the incident. The continuous post-event monitoring aims to detect the variations in the size and the shape of the hemorrhage regions. To this end, the human head is illuminated by non-ionizing electromagnetic radiation, and the scattered field is measured in different time instants. The decomposition of the time-reversal (DORT) method is then used as the microwave imaging algorithm to produce an indicator function. The performance of the proposed technique is assessed via numerical simulations involving a realistic human head phantom. The results suggest that the DORT method is capable of detecting the changes in multiple simultaneous cerebral hemorrhage regions successfully.
Citation - WoS: 1
Citation - Scopus: 5
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.
Citation - Scopus: 1
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.
Regression Analysis of Stock Exchanges During the Ramadan Period: Analysis of 16 Countries
(2016) Tan, A. Serdar; Özlem S.
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Turkish Speech Recognition
(2018) Arısoy, Ebru; Saraçlar, Murat
Automatic speech recognition (ASR) is one of the most important applications of speech and language processing, as it forms the bridge between spoken and written language processing. This chapter presents an overview of the foundations of ASR, followed by a summary of Turkish language resources for ASR and a review of various Turkish ASR systems. Language resources include acoustic and text corpora as well as linguistic tools such as morphological parsers, morphological disambiguators, and dependency parsers, discussed in more detail in other chapters. Turkish ASR systems vary in the type and amount of data used for building the models. The focus of most of the research for Turkish ASR is the language modeling component covered in Chap. 4.
Citation - WoS: 17
Citation - Scopus: 18
Microwave Imaging of Breast Cancer With Factorization Method: Spions as Contrast Agent
(Wiley, 2020) Çayӧren, Mehmet; Coşğun, Sema; Bilgin, Egemen
Female breast at macroscopic scale is a non-magnetic medium, which eliminates the possibility of realizing microwave imaging of the breast cancer based on magnetic permeability variations. However, by administering functionalized, superparamagnetic iron-oxide nanoparticles (SPION) as a contrast material to modulate magnetic permeability of cancer cells, a small variation on the scattered electric field from the breast is achievable under an external, polarizing magnetic field. PURPOSE: We demonstrate an imaging technique that can locate cancerous tumors inside the breast due to electric field variations caused by SPION tracers under different magnetic field intensities. Furthermore, we assess the feasibility of SPION enhanced microwave imaging for breast cancer with simulations performed on a multi-static imaging configuration. METHODS: The imaging procedure is realized as the factorization method of qualitative inverse scattering theory, which is essentially a shape retrieval algorithm for inaccessible objects. The formulation is heuristically modified to accommodate the scattering parameters instead of the electric field to comply with the requirements of experimental microwave imaging systems. RESULTS:With full-wave electromagnetic simulations performed on an anthropomorphically realistic breast phantom, which is excited with a cylindrical imaging prototype of 18 dipole antenna arranged as a single row, the technique is able to locate cancerous tumors for a experimentally achievable doses. CONCLUSIONS: The technique generates non-anatomic microwave images, which maps the cancerous tumors depending on concentration of SPION tracers, to aid the diagnosis of the breast cancer.
Citation - Scopus: 1
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.
Citation - WoS: 4
Citation - Scopus: 4
Multi-Stream Long Short-Term Memory Neural Network Language Model
(2015) Saraçlar, Murat; Arısoy, Ebru
Long Short-Term Memory (LSTM) neural networks are recurrent neural networks that contain memory units that can store contextual information from past inputs for arbitrary amounts of time. A typical LSTM neural network language model is trained by feeding an input sequence. i.e., a stream of words, to the input layer of the network and the output layer predicts the probability of the next word given the past inputs in the sequence. In this paper we introduce a multi-stream LSTM neural network language model where multiple asynchronous input sequences are fed to the network as parallel streams while predicting the output word sequence. For our experiments, we use a sub-word sequence in addition to a word sequence as the input streams, which allows joint training of the LSTM neural network language model using both information sources.
Live Demo: Design and Fpga Implementation of a Component Level Uav Simulator
(IEEE, 2023) Aydın, Yusuf; Ayhan, Tuba; Akyavaş , İrfan
In this work, we introduce a fast, component based simulation environment for UAVs. The simulator framework is proposed as combination of three sub-models: i. battery, ii. BLDC and propeller, iii. dynamic model. The model parameters are extracted for a particular UAV for testing the simulator. The simulator is implemented on an FPGA to increase simulation speed. The simulator calculates battery SOC, position, velocity and acceleration of the UAV with gravity, drag, propeller air inflow velocity. The simulator runs on the FPGA fabric of XilinxXCKU13P with simulation steps of 1 ms.
Citation - WoS: 22
Audio Source Separation Using Variational Autoencoders and Weak Class Supervision
(Institute of Electrical and Electronics Engineers (IEEE), 2019) Kırbız, Serap; Karamatlı, Ertuğ; Cemgil, Ali Taylan
In this letter, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.
Citation - Scopus: 5
A Framework for Automatic Generation of Spoken Question-Answering Data
(Association for Computational Linguistics (ACL), 2022) Manav, Y.; Menevşe, M.Ü.; Özgür, A.; Arısoy, Ebru
This paper describes a framework to automatically generate a spoken question answering (QA) dataset. The framework consists of a question generation (QG) module to generate questions automatically from given text documents, a text-to-speech (TTS) module to convert the text documents into spoken form and an automatic speech recognition (ASR) module to transcribe the spoken content. The final dataset contains question-answer pairs for both the reference text and ASR transcriptions as well as the audio files corresponding to each reference text. For QG and ASR systems we used pre-trained multilingual encoder-decoder transformer models and fine-tuned these models using a limited amount of manually generated QA data and TTS-based speech data, respectively. As a proof of concept, we investigated the proposed framework for Turkish and generated the Turkish Question Answering (TurQuAse) dataset using Wikipedia articles. Manual evaluation of the automatically generated question-answer pairs and QA performance evaluation with state-of-the-art models on TurQuAse show that the proposed framework is efficient for automatically generating spoken QA datasets. To the best of our knowledge, TurQuAse is the first publicly available spoken question answering dataset for Turkish. The proposed framework can be easily extended to other languages where a limited amount of QA data is available. © 2022 Association for Computational Linguistics.
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
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