TronixLab PH

TronixLab PH Bringing ideas into reality. Let's work together to create electronics prototypes.

17/07/2026

Our team developed Project REACH (Radar-Enabled Access to Communication for the Hearing-Impaired Students), an innovative device that uses millimeter-wave (mmWave) radar to capture Filipino Sign Language (FSL) gestures without requiring wearable gloves or computer vision. The radar sensor scans the signer's hand and arm movements. The radar detects these physical changes even in dark environments and transmits the data to a connected device, which decodes the gesture into text and speech. Employs machine learning models to instantly translate FSL gestures into spoken audio and readable text. Displays translations visually, making it easier for hearing-impaired individuals to communicate with abled individuals in real time.

Working on a new project with RAK Wireless LoRaWAN for smart agriculture. This is my first time experimenting with LoRaW...
17/07/2026

Working on a new project with RAK Wireless LoRaWAN for smart agriculture. This is my first time experimenting with LoRaWAN using RAK Wireless, which offers simple network and device configuration, an Arduino IDE-supported sensor node board for easy programming, and WizTool kit software for configuring devices without reprogramming. The gateway Wizgate Edge Pro V2 supports MQTT, enabling the network to easily integrate with the customized system application via an MQTT broker.

From Sensor Data to Real-Time Intelligence on the Edge — Human Activity Recognition Project. Excited to share one of my ...
21/05/2026

From Sensor Data to Real-Time Intelligence on the Edge — Human Activity Recognition Project. Excited to share one of my recent projects in Tiny Machine Learning (TinyML): an end-to-end Human Activity Recognition (HAR) system deployed on the Arduino Nano 33 BLE Sense using the framework.

🔬 What this project does
✅ The system recognizes real-time wrist/hand motions directly on-device.
✅ No cloud. No server. Just real-time inference at the edge.

⚙️ Technical pipeline
* Data acquisition: 3-axis IMU accelerometer data sampled at 100 Hz
* Signal processing: digital low-pass filter, sliding window, FFT spectral analysis, feature engineering.
🤖 Machine learning
* Lightweight neural network classifier trained in TensorFlow/Keras
* Model exported with
* Runs directly on the embedded microcontroller for real-time inference

Projects like this highlight the exciting intersection of machine learning, embedded systems, and digital signal processing.

We recently conducted an Internet of Things (IoT) Workshop for science high school and college students, introducing the...
23/01/2026

We recently conducted an Internet of Things (IoT) Workshop for science high school and college students, introducing them to hands-on IoT development using our locally developed IoT Trainer Board platform.

The session focused on practical learning — from sensor interfacing and data acquisition to cloud connectivity and real-time monitoring. Students were able to explore IoT concepts through actual implementation using a trainer board designed to be versatile, user-friendly, easy to use, and fault-tolerant, making it ideal for both beginners and advanced learners.

It is inspiring to see young learners quickly grasp IoT concepts when supported by the right learning platform and guided instruction.

We are committed to empowering future students and innovators through accessible and effective IoT education tools.

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