Get to Know Me as a Manager
My competencies include market research for new products, strategic planning, in-house product/quality training, field/dealer training, managing the transition and compliance process with the MDR regulation, ISO 9001 internal auditing, and developing and implementing the quality management system within the company. Additionally. I am young, dynamic, eager to learn, responsible, willing to take risks, successful in teamwork, and highly skilled in leading a team.
PROJECTS
Purpose and Function of the Project:
This project aims to develop an adjustable power supply that can provide various voltage levels required for different electronic applications. The adjustability range of 0-30V makes this power supply versatile for use in laboratories, research and development projects, and hobby electronics.
Main Components and Their Functions:
Transformer (TR1): Converts the high voltage from the electrical network to a lower, safer level that the circuit can handle.
Bridge Diode (BR1): Converts AC (Alternating Current) voltage to DC (Direct Current) voltage.
Capacitor (C1 - 2200uF): Smooths and filters the rippled DC voltage coming from the diode bridge.
Voltage Regulator (Q1, Q2, Q3, Q4): Provides an adjustable output voltage. Q2 and Q3 transistors enhance voltage boosting and high current carrying capability, while Q1 controls the voltage regulation.
Potentiometer (RV1 - 0.25k): Used to adjust the output voltage.
Resistors and Other Components: Limit current and voltage to ensure the circuit functions correctly.
Working Principle:
The reduced voltage from the transformer is rectified by the bridge diode and then filtered by the capacitor. The potentiometer adjusts the current passing through the base transistor to control the output voltage. The adjustable voltage is applied to the load by the output transistors.
Design and Simulation:
The simulation conducted using Proteus software checks if the circuit elements are correctly assembled, whether the circuit performs its expected function, and identifies and corrects any errors at an early stage. Simulation plays a crucial role in verifying theoretical designs before assembling an actual circuit.
VersaVolt
5
Project Aim:
The aim of this project is to classify sleep stages using EEG (Electroencephalography) and EMG (Electromyography) signals with artificial neural networks and to evaluate the accuracy of this classification.
Procedures:
1. Data Collection:
- Data was obtained from a 47-year-old female patient from the Physiobank ATM database. Approximately 9 hours of EEG and EMG data were used.
2. Data Preprocessing:
- The raw data collected was processed and prepared for analysis.
3. Epoch Division:
- The signals were divided into thirty-second epochs for sleep staging, resulting in a total of 1079 epochs.
4. Feature Extraction:
- Features were extracted using both the time domain and the time-frequency (hybrid) domain. Calculated features included mean, standard deviation, variance, mean energy, mean curve length, and mean Teager energy.
5. Classification:
- The K-Nearest Neighbor (KNN) algorithm was used to classify the data into wakefulness (W), NREM (N1, N2, N3), and REM (R) stages.
6. Performance Evaluation:
- The performance of the classification was evaluated by calculating accuracy, sensitivity, specificity, F-measure, and AUC (Area Under the Curve).
Signals Used:
- EEG (Electroencephalography) Signals:Measure the electrical activity of the brain.
- EMG (Electromyography) Signals:Measure the electrical activity of the muscles.
Software Used:
- MATLAB: Used for data processing, feature extraction, and classification tasks.
Artificial Intelligence Algorithms Used:
- Artificial Neural Networks (ANN): Used for the classification of sleep stages.
- K-Nearest Neighbor (KNN) Algorithm: Used for data classification.
Results:
The classification of sleep stages using EEG and EMG signals yielded the best performance with the Weighted KNN algorithm in the time domain. The results of this study demonstrate that sleep stages can be accurately classified. High performance was achieved as evidenced by the calculated values for accuracy, sensitivity, specificity, F-measure, and AUC. This indicates that artificial neural networks and KNN algorithms can be effectively used for the classification of sleep stages.
Classification of Sleep Stages
2
Purpose and Function of the Project:
The aim of this project was to build a sumo robot designed to compete in a sumo wrestling arena. The robot's objective is to push its opponent out of a circular ring, which has a white line marking the boundary.
Key Specifications:
The robot must not exceed a certain weight and height limit.
The project was built using an Arduino microcontroller.
The robot's chassis was designed in SolidWorks and 3D printed.
Main Components and Their Functions:
Arduino Microcontroller: Acts as the brain of the robot, controlling all movements and sensors.
Motors and Wheels: Provide the necessary movement and maneuverability.
Sensors: Detect the edge of the ring and the opponent's position.
Chassis: 3D printed to provide a sturdy and lightweight frame for the robot.
Working Principle:
The robot uses its sensors to detect the white boundary line and avoid crossing it. It also uses sensors to locate and push the opponent out of the ring. The Arduino microcontroller processes the sensor inputs and controls the motors accordingly.
Design and Fabrication:
The robot's design was created in SolidWorks and printed using a 3D printer. The Arduino code was written and tested to ensure the robot could effectively navigate the ring and engage with the opponent.
Project Execution:
Design: The robot's chassis was carefully designed to balance strength and weight, ensuring it could meet the competition's requirements.
3D Printing: The designed parts were printed using a 3D printer.
Assembly: The components were assembled, and the Arduino was programmed to control the robot's actions.
Testing: The robot was tested in various scenarios to refine its performance and ensure it could successfully compete.
Sumo Competition Robot
4
Mission:
Flight and maneuverability in designated tasks
Autonomous flight
Identify and collect 250 grams of water from a specified location, land, and fill a container
Transport and discharge the collected water at a designated target location
Location - Date:
Gaziantep Alleben Pond (competition held from September 15-20, 2020)
Gaziantep (TEKNOFEST Award Ceremony held on September 26-27, 2020)
University Drone Project:
As part of the International Unmanned Aerial Vehicle (UAV) Competition organized by TÜBİTAK, a drone team was formed at our university. I served as the team captain of this project. The design and production processes of the drone were carried out using SolidWorks, and all parts were produced by our team using a 3D printer.
The engineering calculations for the drone's weight-lifting capacity, wind resistance, flight duration, and maneuverability were meticulously conducted. The design of the electronic circuitry and the development of the software were also successfully completed by our team members. In this context, we developed a drone capable of performing all required tasks efficiently.
Versatile UAV Development
3
Autonomous Cargo Drone
Project Aim:
The aim of this project is to design and develop an autonomous drone capable of providing courier and cargo services within predefined urban areas. The project seeks to innovate in the logistics sector by leveraging drone technology for efficient, timely, and cost-effective delivery services.
Drone Station:
The drone station serves as the central hub for drone operations, including charging, maintenance, and dispatch. It ensures the readiness of drones for immediate deployment and manages the logistical aspects of the drone delivery system.
Payload Capacity:
Maximum payload capacity: 3000 grams
Drone Specifications:
Design:
Frame: Constructed from high-strength materials such as carbon fiber and HDPE
Battery compartment: Designed for both summer and winter use, ensuring optimal performance in various weather conditions
Safety features: Equipped with a parachute system to minimize impact in case of failure
Software Used:
SolidWorks: For designing and simulating the mechanical components of the drone.
MATLAB: For processing sensor data, performing calculations, and simulating flight dynamics.
e-Calc: For verifying flight performance and battery endurance calculations.
Artificial Intelligence Algorithms Used:
Machine Learning: Implemented for optimizing flight paths and enhancing autonomous navigation.
Computer Vision: Used for real-time object detection and obstacle avoidance.
Project Manager's Role:
As the project manager, I was responsible for overseeing the entire project lifecycle, from initial concept to final testing and deployment. This included coordinating the design and development processes, ensuring timely progress, and managing the project team.