I'm a machine learning and embedded engineer in Rovaniemi, just south of the Arctic Circle, where I lead the high-speed drone project at Frostbit, Lapland University of Applied Sciences. Most of my work sits where code turns into motion: a drone that hit 287 km/h, a car that drives itself, and a little frog that keeps plants alive.
The fastest pass of a 79-second test flight on 24 September 2026, low over forest and bog outside Rovaniemi.
Peak GPS ground speed in level flight at full throttle. GPS Doppler and position-derived speed agree to within 0.1 km/h.
At 24,100 rpm, an 8-inch pitch gives 294 km/h in theory. The drone reached 97–98% of it, so the next build moves to higher voltage for more RPM.
Four passes went above 260 km/h. Around the peak, two independent GPS measurements matched within 0.1 km/h, with 16 satellites locked.
The pack sagged on every hard pass and collapsed at 66 seconds. The drone came down in bushes 143 m from takeoff, and the log shows why.
Hardware, models, and the data between them, mostly with real partners.
A hand-painted frog you push into a plant's soil. It measures moisture, light and warmth and tells your phone before the plant wilts. It started as a university project between FH Technikum Wien and Lapin AMK. My part is the AI and the circuit boards, which I design and solder.
Three models run on the live camera feed: TinyUNet keeps it in lane, YOLOv5 watches for objects, and a Random Forest adjusts speed for the weather. 92% average lane model accuracy.
Computer vision for a miniature log-loading machine that students practise on. It counts logs, reads their orientation and times each placement, so every practice run gets measurable feedback. I also install the hardware on site and keep it running smoothly.
Local rainfall prediction from weather sensors at four sites. As the team's machine learning specialist, I led model development and tested around ten classical and deep learning approaches.
An interactive map of where heavy trucks meet hazards in cities. They cluster around turning, merging and braking, where traffic is densest.
Coursework models, prototypes and the code behind the projects above.
github.com/Saadtariq32Upgrading the setup and refining everything for the next speed runs.
Log detection for MetsäDigi's training machine, due before I graduate in January 2027.
Circuit boards and the AI assistant, with a team split between Vienna and Rovaniemi.
I like problems that cross the line between code and physical things, where a model's output ends up as a motor command, and the only honest test is a flight log or a lap of the track.
I started in electronics at Metropolia in Helsinki, then moved north to Rovaniemi for machine learning and data engineering. Lapland gave me the opportunity to study my passion and experience it first-hand.
Programming and AI: Python, C/C++, SQL, machine learning, computer vision, LLMs
Hardware: drone systems, Betaflight, Raspberry Pi, ESP32, PCB design and soldering
Data and tools: Power BI, Docker, Git, Linux, flight log analysis
Frostbit, Lapland UAS. Flight test report.
A high-speed interceptor built around a 3D-printed streamlined shell, a 6S power system and a flight controller that logs every motor's RPM. Every flight is tested, logged and taken apart in the data. This page walks through the speed run of 24 September 2026.
Vertical speed comes from GPS altitude, RPM is the average of the four motors, and pitch speed is what an 8-inch pitch would give at that RPM.
| Pass | Time | GPS speed | Heading | Climb | Throttle | RPM | Pitch speed | Pack |
|---|---|---|---|---|---|---|---|---|
| A | 24.9 s | 277.7 km/h | 87° E | +7.6 m/s | 97% | 23,700 | 289 km/h | 20.3 V |
| B | 33.1 s | 264.7 km/h | 293° WNW | +8.8 m/s | 87% | 22,200 | 271 km/h | 21.0 V |
| C | 57.7 s | 272.5 km/h | 282° W | −1.4 m/s | 97% | 23,500 | 287 km/h | 20.4 V |
| D | 61.0 s | 287.4 km/h | 289° WNW | −1.0 m/s | 100% | 24,100 | 294 km/h | 20.2 V |
Over the 1.4 seconds around the peak, the mean GPS Doppler speed was 286.5 km/h and the speed from successive GPS positions was 286.4 km/h, with 16 satellites locked.
The drone was descending at about 1 m/s at 80 m/s ground speed, a descent angle under 1°, so gravity added a km/h or two at most.
The best eastbound pass was climbing at 7–8 m/s, which costs speed, while the best westbound pass was level. Averaging the two gives about 282 km/h, so the true still-air level speed sits between 282 and 287.
For a record-style figure, the next run flies back-to-back level passes both ways at the same height and throttle.
At 24,100 rpm an 8-inch pitch gives 294 km/h in theory, and the drone reached 97–98% of it. Smoothing the shell won't add much on 6S. More RPM will, which is why the next build moves to higher voltage and upgraded batteries.
Around the peak, GPS Doppler speed and speed derived from logged positions matched within 0.1 km/h, with 16 satellites and a descent angle under 1°. The number holds up.
The pack sagged to 3.35 V per cell on every hard pass, then collapsed at 66 seconds. The drone lost authority and came down in bushes 143 m from takeoff.
24,100 rpm ÷ 60 × 0.2032 m = 81.6 m/s = 294 km/h
The propeller runs at an advance ratio of about 1.42, almost equal to its pitch-to-diameter ratio of 1.45 (8 ÷ 5.5), so it makes very little thrust at top speed. The motors reached about 85% of KV × sagged voltage.
On 6S, top speed is set by RPM × pitch, so a smoother shell won't add much. More RPM will. With higher voltage and upgraded batteries the limit shifts to drag, and that's where the AirShaper study of the shell decides the outcome.
First GPS-logged flight, at about 76% throttle.
Full throttle, level pass. Four passes above 260 km/h.
Upgraded batteries and more RPM, with drag measured and checked against the AirShaper simulation.
Armed at 24.8 V (4.13 V per cell). The pack wasn't fully charged.
From here on, every hard pass sags the pack to about 20 V, so the low-battery warning stays on as background noise.
Peak speed, 287.4 km/h at 20.2 V.
Full-throttle punch. Voltage falls from 18.9 V to 12 V in about two seconds, and RPM falls with it.
The pack reaches 8.4 V. One motor pins at minimum while the others run near maximum, and the drone can't hold altitude.
Comes down into bushes at about 48 km/h, 143 m from home.
Share of full-throttle time each motor spent pinned at maximum output.
Motor 1 sat at maximum 55% of the time, the others 23–32%. Its RPM is slightly higher than the rest, which points to centre of gravity or trim rather than a failing motor.
The logged current showed about 8 A at full throttle and only 152 mAh for a flight that drained the pack, so this flight's power figures can't be used.
Once it's calibrated, the watts needed to hold top speed give the drag area directly, to compare with the AirShaper simulation and refine the next prediction from measured numbers.
About 45–50 seconds or a fixed number of passes, instead of trusting voltage warnings that sit near the line all flight.
It read about ten times low. Real power at 287 km/h gives the drag area to compare with AirShaper.
Lower internal resistance and 4.2 V per cell before any record attempt.
It sat at maximum output twice as often as the others. Check the centre of gravity and swap props.
How the numbers were checked: the Betaflight blackbox log was decoded with a custom parser, then cross-checked against 10 Hz GPS and the goggle video, aligned to within half a second.