Ahenk Vural Dalkiran

Optimized low power consumption Engineered a robust, CI/CD automated testing infrastructure by developing Hardware-inthe- Loop (HIL), Software-in-the-Loop (SIL), and unit tests, improving firmware validation efficiency. Debug & CI Improvement: Enhanced debugging and CI/CD test capabilities by integrating JLink debugger Joulescope, integrated to CI/CD, automatically track power consumption metrics. Utilized key peripherals including I2C, SPI, UART, ADC, and DAC for complex system integration.

Led the design and development of embedded systems for high-security crypto devices utilizing real-time operating systems (RTOS). Managed critical security features including tamper detection, emergency erase, and secure key preservation for cryptographic communications, ensuring device integrity via enviromental sensors. Prepare several sensor libraries in C programming language Gained deep expertise working with low-power microcontroller families such as STM32 L series, PSOC, and MSP430.

Master’s Thesis: Deep Learning-Based Super-Resolution Applications in Image Processing Key Focus: Implemented and compared deep learning methods (SRGAN, EDSR, SAN, RCAN, CAR) against classical methods like bicubic interpolation.

Master’s Thesis: Deep Learning-Based Super-Resolution Applications in Image Processing Key Focus: Implemented and compared deep learning methods (SRGAN, EDSR, SAN, RCAN, CAR) against classical methods like bicubic interpolation.

Complete computer engineering courses besides fundamental courses about electrical and electronics engineering. Bachelor’s Thesis: E-Dog, an assistive device for helping visually impaired people. Design hardware and firmware.