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Akhila Chitturi
Mid-level Embedded Software Engineer specializing in real-time control and automated testing
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About
Master’s thesis researcher building an intelligent fault diagnosis and predictive maintenance stack for autonomous quadcopters—covering simulation-based fault injection, signal processing (Id/Iq), ML fault classification, and real-time edge deployment on Raspberry Pi with Hailo-8 acceleration. Previously delivered production C++ middleware/microservices at Accolite and has hands-on experience with constrained networking via a LoRaWAN IoT communication stack.
Experience
Education
Key Strengths
- Built an end-to-end, automated fault diagnosis pipeline for autonomous quadcopter BLDC motors from scratch
- Strong data engineering for high-frequency time-series signals (preprocessing, resampling, normalization)
- Bridged sim-to-real gap using targeted augmentation (noise injection, scaling, FFT-based analysis)
- Applied motor control domain knowledge (Id/Iq via Park transformation) to improve fault identification
- Real-time safety-loop optimization by profiling bottlenecks and offloading inference to Hailo-8 NPU with quantization
- Created reproducible simulation-driven datasets via fault injection in PSIM and MATLAB/Simulink
- Experience designing reliable communication stacks in constrained networks (LoRaWAN: packet loss/latency/duty-cycle constraints)
- Production engineering background containerizing C++ microservices with Docker and contributing to CI/CD test automation
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