IoT-Based USD to IDR Exchange Rate Display Using Bank BCA Exchange Rates
Real-Time Bank BCA USD/IDR Exchange Rate Monitor using ESP32 and OLED Display with live internet updates.
Features:
• Real-time USD/IDR exchange rate display
• ESP32 WiFi connectivity
• OLED SSD1306 interface
• Automatic online data refresh
• Lightweight and low-power system
• Compact embedded monitoring device
#ESP32 📡 | #IoT 🚀 | #EmbeddedSystems 💻 | #Arduino ⚙️ | #Programming 📚
1
244
24V BLDC Motor Controller with Field-Oriented Control (FOC)
Designed a custom 24V three-phase BLDC motor controller PCB for precision robotic motion control using Field-Oriented Control (FOC). The project focused on creating a modular, high-performance embedded hardware platform capable of delivering accurate torque, speed, and position control for robotic joints, gimbal systems, and industrial automation applications.
Key Features
1) Custom schematic design for a 24V FOC motor controller
2) STM32G431-based embedded control architecture
3) Three-phase MOSFET inverter driven by a DRV8353 gate driver
4) High-resolution magnetic encoder interface (SPI) for closed-loop position feedback
5) Three-phase current sensing using precision shunt resistors and current-sense amplifiers
6) CAN Bus communication for integration into distributed robotic systems
7) Integrated protection circuitry, including reverse-polarity protection, over-current protection, and transient voltage suppression
8) Dedicated SWD programming and debugging interface
9) Optimized power distribution for separate 24V, 5V, and 3.3V domains
Project Outcome
The design provides a complete hardware platform for implementing advanced FOC algorithms capable of smooth, efficient, and highly accurate BLDC motor control. The architecture is suitable for robotics, precision actuators, autonomous systems, industrial automation, and research applications requiring high-performance motion control.
0
222
This is a project that I titled Low-cost AI Power theft detection that detects electricity theft, bypass of electricity, high electricity usage, with the help of TinyML integrated and trained into the esp32 and alert with the help of the buzzer and shows in a well polished web dashboard, sensor used would be communicated after the project as been finally built and ready for implementation.
0
84
Raspberry Pi CM5 Carrier Board with Class-D Amp
In this design, Texas Instruments TPA3118 Class-D amplifier is integrated on the PCB carrying Raspberry Pi CM5. Power supply is coming from USB-PD input via Type-C port.
I/O ports
1 x HDMI
1 x DSI 22p FPC connector
2 x USB 3.0 Type-A
2 x USB 2.0 Type-A
1 x M.2 M-Key PCIe slot for SSD
1 x 3.5mm stereo Line-out jack
1 x USB Type-C Debug/Flash
1 x USB Type-C PD
1 x PWM Fan connector
1 x RGB LED 3-p connector connected to GPIO
1 x rotary encoder input connected to GPIO
ICs
Texas Instruments PCM5102A, 2.1 VRMS, 112/106/100 dB Audio Stereo DAC with PLL and 32-bit, 384 kHz PCM Interface
Texas Instruments TPA3118D2, 30-W stereo, 60-W mono, 4.5- to 26-V supply, analog input Class-D audio amplifier
Injoinic IP2721, TYPEC/PD2.0/PD3.0 Physical Layer IC for USB Type-C input Interfaces
Terminus Tech FE1.1s, USB 2.0 High Speed 4-port Hub Controller
0
95
This is a project I titled: Development of High Blood Pressure (HBP) Prediction and Monitoring System Using CNN-BiLSTM and Fuzzy Logic in Real Time
Still undergoing development:
1. The Flask backend is operational with an integrated CNN-BiLSTM prediction pipeline and a fuzzy logic module for health risk assessment, along with REST API endpoints for communication.
2. A Flutter mobile application has been developed, featuring a dashboard that displays various health metrics and successfully communicates with the backend.
3. Hardware development includes the successful configuration of the ESP32 microcontroller, integration of an OLED display, and testing of the MAX30102 PPG sensor for real-time waveform acquisition.
4. AI integration involves transmitting real PPG data from the MAX30102 sensor to the Flask backend, where the CNN-BiLSTM model generates blood pressure predictions, which are then returned to the ESP32 and backend.
5. The complete communication pipeline from sensor to monitoring dashboard has been established, confirming successful real-time signal processing for blood pressure prediction.
6. Ongoing work focuses on sensor stabilization, hardware integration, synchronization of live updates, alert mechanisms, power management, and final system packaging.
7. The core real-time HBP prediction system using CNN-BiLSTM is implemented and tested, with the project now in the optimization stage before final deployment.
Willing to hear suggestions and getting more gigs to work with and solving of problems
0
63
Arduino Nano-Based PCB Design
I designed a custom PCB built around the Arduino Nano, creating a compact hardware platform for integrating the microcontroller with supporting electronic components and external peripherals.
The design involved organizing the Arduino Nano interface, power connections, input/output lines, and supporting circuitry into a clean and practical PCB layout. I focused on efficient component placement, clear signal routing, reliable power distribution, and maintaining a layout that is easy to assemble and integrate into a larger embedded system.
This project strengthened my experience in Arduino-based hardware development, PCB layout, embedded electronics, component integration, and microcontroller interfacing.
0
100