Ismail Saritas

Electroencephalogram (EEG) signals are obtained from the surface of the scalp through a Brain-Computer Interface (BCI) and show the activity of brain regions. EEG signals are useful tools for people, especially those who have severe motor disabilities for an improved quality of life. Because of their noisy nature caused by a small movement of the head, eye blink, or even breathing, it is extremely hard to extract meaningful information from EEG signals. Thus, it is imperative to filter EEG signals without losing their essential parts. While filtering, the concept of adaptivity comes in handy because of its flexible nature to preserve important information. In this context, a novel approach that applies Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD), or Variational Mode Decomposition (VMD) methods to filter EEG signals adaptively based on correlation was proposed. The performances of developed methods were calculated based on the accuracies of binary classifications of five different labels by using machine learning classifiers and compared with an Elliptic Bandpass Filter. The study shows that the proposed adaptively implemented VMD is the most useful filtering method for EEG signals, providing the best performance with 76.1% subject-wise accuracy for binary classification of word association and feet motor imagery and an average accuracy of 70.4% for all binary classifications using a Support Vector Machine (SVM) classifier. The results show that correlation is an efficient tool to adaptively implement signal decomposition methods as filters by preserving meaningful information more successfully than an Elliptic Bandpass Filter.

Monitoring patients' vital signs such as pulse, body temperature, systolic-diastolic blood pressure, and SpO₂ plays a significant role in healthcare systems for post-operative patients, emergencies, infants, individuals with heart failure, pandemics, and similar situations. In this study, two different real-time remote monitoring and early warning systems (RTMEWS) are proposed to assist healthcare professionals and physicians in measuring and monitoring patients' vital signs. In the first proposed RTMEWS, SpO₂, pulse, and body temperature can be measured using a pulse oximeter developed with an ATMEGA328P microcontroller and MAX30100 sensor, and monitored via wireless transmission from the OLED GLCD screen on the prototype through an interface developed with the MIT2 application on a smartphone. In the second proposed system, when the measured pulse rate, body temperature, and systolic-diastolic blood pressure findings exceed the threshold range determined by the physician, a warning message can be sent to the mobile phone of the healthcare worker, doctor, or desired recipient via an MC35i terminal (GSM/GPRS modem) controlled by an AT command set connected to the computer where the data is stored. In conclusion, many factors influence the determination of normal and abnormal ranges of vital signs and intervention priorities. When unforeseen situations arise in clinical care processes, RTMEWS has been shown to assist professional interventions by generating timely alarms. The system provides a foundation for clinical decision support systems by offering real-time remote monitoring and autonomous threshold-based alerts.

The integrity of storage tank bottom plates is critically affected by corrosion-related material loss, which poses significant risks to operational safety, environmental protection, and asset reliability. Ultrasonic testing (UT) has long been employed as a non-destructive evaluation technique for thickness measurement; however, reliable interpretation of high-density ultrasonic data remains challenging under real field conditions due to noise, surface irregularities, and complex echo patterns. This study presents an academically oriented framework for the inspection of storage tank bottoms using ultrasonic A-scan data combined with advanced signal processing techniques. Frequency-domain and time–frequency-domain methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and wavelet-based analysis, are employed to enhance signal quality and improve echo discrimination. A hybrid peak detection strategy integrating adaptive thresholding and continuous wavelet transform (CWT) is proposed to robustly identify corrosion-related reflections. The extracted thickness data are spatially organized on two-dimensional grids and transformed into three-dimensional corrosion maps using interpolation-based reconstruction techniques. The proposed methodology demonstrates improved robustness in low signal-to-noise ratio (SNR) environments and contributes to a more objective and repeatable corrosion assessment process. The results highlight the potential of signal processing–driven UT analysis as a reliable academic and industrial tool for storage tank integrity evaluation.

Electroencephalogram (EEG) signals are very suitable for Brain-Computer Interface (BCI) applications due to their cost-effectiveness and high mobility. Although EEG signals have a high temporal resolution, their spatial resolution is low. In this study, four-class Motor Imagery (MI) EEG signals of an individual who received 20-day BCI training were analyzed with the topographic brain mapping method to examine the spatial distribution of EEG. The analysis was performed by examining each EEG waveband for each second of the MI task. As a result, Delta and Theta bands related to concentration and learning showed activity in the first seconds. For Alpha, Beta, and Gamma activities, processes related to active thought occurred in the first seconds, and processes related to decision-making happened in the last seconds. When the activities of EEG bands for each electrode channel are determined, the success in BCI applications can be increased. The study has the potential to be a reference for other studies on paradigm creation and improvement.

Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.

Classification is an important technique used to predict which group the samples in the data belong to. In this study it is aimed to classify according to emotions by extracting audio features. The study was performed using audio data from 2 male and 2 female individuals that spoke in four different emotions amused, angry, neutral, and sleepy. “MFCC” as a Cepstral feature, “Centroid, Flatness, Skewness, Crest, Flux, Slope, Decrease, Kurtosis, Spread, Entropy, roll-off point” as Spectral Feature, “Pitch, Harmonic ratio” as Periodicity Features were used in voices. All classification algorithms that were located in the classification learner toolbox in Matlab were applied to the data, and the algorithm providing the highest accuracy was asked to estimate the emotion. 26 inputs and one output were calculated, and the performance results had compared to each other. According to the results, it has been seen that the support vector machine algorithm provides the highest accuracy performance. Considering the performances obtained, this study reveals that it is possible to distinguish and classify sounds using sentimental data and sound feature parameters.

Patients with chronic respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD) receive long- term oxygen therapy (USOT) to sustain their lives [1],[2]. With the development of oxygen concentrator (OC) devices that can produce the concentrated oxygen required for USOT, COPD patients are required to use these devices for more than 12 hours daily depending on the prescription [3],[4]. OC are medical devices that separate oxygen from the atmosphere using physical means to produce concentrated gas for medical purposes [5],[6]. The use of conventional motors based on the permanent magnetic rotary motor operating principle in OC devices increases the mass of the device and the operating noise disturbs the patients [3],[4],[7]. In this study, with the advances in magnet material, a tubular linear motor (TLM) structure with a strong, fixed coil moving permanent magnet, which is stronger than the linear motor used in many fields such as medical electronics, nanotechnology, defence industry, maglev trains, is designed and proposed for use in OK devices. It is difficult to optimise the TLM due to multiple design parameters and each parameter has a non- linear relationship with the static electromagnetic force. In this study, the thrust of the TLM is optimised by the finite element method (FEM) using the magnetic magnetostatic and transient solvers in Ansys Maxwell3D. Optimisation method based on FEM 3D model was used to optimise the design parameters. Comparing the pre- and post-optimisation of the TLM designed for use in the OC device, the thrust force was increased from 567.91 fN to 5.82 nN at the same working stroke distance.