Dal, Barış

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Profile URL
https://hdl.handle.net/20.500.14365/7378
Name Variants
Dal, Baris
Job Title
Email Address
baris.dal@ieu.edu.tr
Main Affiliation
05.11. Mechatronics Engineering
Status
Former Staff
Website
ORCID ID
0000-0003-2531-2556
Scopus Author ID
57218589248
Turkish CoHE Profile ID
Google Scholar ID
2nVg2wUAAAAJ
WoS Researcher ID
HHY-7304-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
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Documents

2

Citations

31

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Scholarly Output

3

Articles

0

Views / Downloads

6/77

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

10

Scopus Citation Count

31

Patents

0

Projects

0

WoS Citations per Publication

3.33

Scopus Citations per Publication

10.33

Open Access Source

1

Supervised Theses

1

JournalCount
2022 Medıcal Technologıes Congress (Tıptekno'22)1
2Nd Internatıonal Congress on Human-Computer Interactıon, Optımızatıon And Robotıc Applıcatıons (Hora 2020)1
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  • Thumbnail Image
    Conference Object
    Citation - WoS: 3
    Citation - Scopus: 18
    Comparison of Different Machine Learning Techniques for the Cuffless Estimation of Blood Pressure Using Ppg Signals
    (IEEE, 2020) Kilickaya, Sertac; Guner, Aytug; Dal, Baris
    Blood pressure (BP) is currently measured using sphygmomanometers, and it is a crucial biomarker of a person's heart health. Hence, regular monitoring of blood pressure is important for early diagnosis and treatment. On the other hand, conventional blood pressure measurement devices discomfort patients, since the blood flow is cut off with the pressure exerted by the cuff while measuring systolic blood pressure. Nowadays, researchers are using different signals such as Electrocardiogram (ECG) and Photoplethysmography (PPG) to extract useful information like pulse arrival time (PAT) and pulse transit time (PTT) in order to estimate blood pressure without using a cuff. Two signals can be used simultaneously, but this method requires two sensors, which makes it expensive and unpractical. To overcome this, only PPG-based cuffless and continuous monitoring of blood pressure has been proposed in several studies. In this paper, in order to estimate systolic and diastolic blood pressure values, three different machine learning algorithms, i.e. Linear Regression (LR), Support Vector Regression (SVR) and Artificial Neural Networks (ANNs), were implemented using PPG signals and some other features such as body mass index (BMI), age, height and weight obtained from the patient. A new, short-recorded photoplethysmogram dataset was used for this purpose, and the results are compared in terms of mean absolute error.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 7
    Citation - Scopus: 13
    Fixed-Point Fpga Implementation of Ecg Classification Using Artificial Neural Network
    (IEEE, 2022) Dal, Barış; Askar, Murat
    Cardiovascular diseases (CVDs) are one of the major causes of mortality around the world. Hence, regular monitoring of electrocardiogram (ECG) signals is crucial for early diagnosis and treatment. This leads to the ASIC/FPGA implementation of ECG classification. The currently suggested FPGA developments depend on statistical analysis of ECG signals to extract some features as the input for the classification network. However, feature extraction methods may cause some information loss. Therefore, an Artificial Neural Network (ANN) model that takes raw input data has been proposed in this work. The MIT-BIH arrhythmia dataset is used for the training and validation of the model. The proposed architecture consists of 2 hidden layers and an output layer. The training achieves around 97% accuracy. The network parameters (weights and biases) are extracted from the trained model as 32-bit floating-point numbers and converted into fixed-point numbers (8-bit) for efficient mapping to the FPGA. Then, the mathematical model of the feed-forward network was developed on Xilinx Zybo FPGA using Verilog HDL. The whole procedure is completed in 232 clock cycles.
  • Thumbnail Image
    Master Thesis
    Fpga Implementation of 1d Convolutional Neural Network for Early Detection of Bearing Faults in Induction Motors
    (İzmir Ekonomi Üniversitesi, 2022) Dal, Barış; Aşkar, Murat
    Asenkron Motorlar, stabilite, düşük maliyet ve kolay bakım sağladıkları için çeşitli endüstriyel uygulamaların temel parçasını oluşturmaktadır. Asenkron motorların arızalanması, üretim zincirinin yavaşlamasına neden olarak ciddi bir para kaybına neden olabilir veya çevreye ve insan sağlığına zararlı olabilir. Asenkron motorlar, dönen parçalar arasında sürtünmeyi azaltıp, hız ve performansı artıran rulmanlar içerir ve bu rulmanlardaki arızalar en çok karşılaşılan motor arızalarıdır. Bu arızaların erken tespiti, sonradan oluşabilecek büyük problemlerle uğraşmak yerine, motorun çok daha ucuza tamir edilmesine veya değiştirilmesine olanak sağlar. Rulman hatalarının tespiti için literatürde farklı yaklaşımlar bulunmaktadır, ancak 1B-Evrişimsel Sinir Ağı (ESA) kullanarak sorunu çözmek için Alanda Programlanabilir Kapı Dizisi (FPGA)/Uygulamaya Özgül Tümdevre (ASIC) tasarımını kuran detaylı bir çalışma bulunmamaktadır. Bu yüksek lisans tezinde, asenkron motorlarda rulman hatalarının erken teşhisi için 1B-Evrişimsel Sinir Ağının FPGA üzerinde uygulaması sunulmuştur. Önerilen model, Case Western Reserve Üniversitesi (CWRU) tarafından sağlanan 4 farklı sınıfa ait (sağlıklı, bilye hatası, iç bilezik arızası ve dış bilezik arızası) titreşim sinyalleri veri kümesini kullanır. Önerilen mimarinin parametreleri, eğitilmiş modelden 32 bitlik kayan nokta sayıları olarak çıkarılır. Daha sonra, FPGA üzerinde bu sayıların depolanması için sayıların sabit nokta (8 bit) temsilleri belirlenir. Sonraki adımda, önerilen modeldeki her katmanın matematiksel eşleniği Verilog Donanım Tanımlama Dili (HDL) kullanılarak geliştirilmiş ve Vivado 19.1 yazılımı kullanılarak Xilinx-ZYBO Z7-10 FPGA kartında gerçeklenmiştir.