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IoT & Embedded Systems Engineer ESP32, Raspberry Pi, Sensors
New to Contra
IoT & Embedded Systems Engineer ESP32, Raspberry Pi, Sensors
Cover image for Title: FlowSense Pro  -
Title: FlowSense Pro - Industrial Flow & Chemical Dosing SCADA Gateway with OPC-UA & BLE Problem: Municipal wastewater treatment plants, breweries, and chemical processing facilities rely on analog 4–20 mA pressure/flow transmitters and mechanical pulse flow meters that lack native network connectivity. Modernizing these field instruments into SCADA platforms historically required dedicated automation racks and programmable logic controllers, inflating per-station deployment costs. Additionally, field technicians in wet, hazardous environments struggle with device commissioning and routine calibration because older systems require physical laptops, terminal blocks, and USB-to-serial cables. Traditional field gateways also lack secure, fail-safe remote firmware updates (OTA), risking operational downtime during software maintenance. Solution: Built a ruggedized, DIN-rail mounted smart gateway around the ESP32-S3 (Xtensa dual-core 240MHz) utilizing ESP-IDF v5.x: High-Precision Dual Measurement: Combines a 16-bit ADS1115 ADC and a 0.1% precision 100 Ω shunt to read 4–20 mA current loops with ±0.5% calibrated accuracy, alongside a hardware pulse counter (PCNT) interrupt engine reading turbine flow meters up to 500 L/min. Direct SCADA & OPC-UA Integration: Compiles the open-source open62541 OPC-UA stack directly into ESP-IDF, enabling the gateway to communicate natively with SCADA servers and industrial HMIs over TCP/IP without intermediate PLC hardware. Cable-Free BLE Commissioning: Implements a custom BLE 5.0 GATT profile that allows field operators to calibrate sensor K-factors, set alarm thresholds, and configure local Wi-Fi credentials via an iOS/Android mobile companion app in seconds. Zero-Power Display & Fail-Safe Actuation: Integrates an ultra-low-power 2.9" SPI E-Paper display that maintains critical process readouts even during brownouts, alongside four opto-isolated 24V/3.5A power MOSFET outputs driving dosing pumps and emergency shutoff valves in <10 ms. Enterprise Security & Robust OTA: Employs mutual-TLS (mTLS) certificate-based authentication with AWS IoT Core and dual-partition HTTPS OTA with automated bootloader rollback upon boot failure.
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Cover image for Title: Smart Assistive Gesture Glove
Title: Smart Assistive Gesture Glove and Caretaker Alert System 1. Problem statement: Speech-impaired, partially paralyzed, or bedridden patients often struggle to communicate basic necessities (food, water, washroom, physical discomfort) or call for immediate emergency help without a caregiver continuously in the room. Standard push-button call systems lack expressive nuance, while cloud-reliant smart devices fail completely whenever local Wi-Fi or internet connectivity drops, posing a critical safety hazard during medical emergencies. 2. Solution and Technical Implementation: ​Gesture-to-Intent Recognition: The patient wears a wearable glove equipped with four flex sensors. Using trimmed-mean signal sampling and Exponential Moving Average (EMA) filtering, the system filters out muscle tremors and maps individual finger flexes to primary needs (Food, Washroom, Water, Discomfort), with a chorded fist gesture reserved for an instant Emergency SOS. ​Zero-Downtime Local Transmission: Communication between the glove and the caregiver base station uses peer-to-peer ESP-NOW radio protocol. This operates completely independent of routers or internet access, featuring an auto-channel hopping algorithm on the glove to maintain the radio link even if radio channels shift. ​Multi-Modal Feedback & Cloud Alerts: The receiver base station converts incoming packets into immediate spoken voice alerts via a DFPlayer Mini MP3 module, visually displays the request on an SSD1306 OLED, and sounds an active buzzer during SOS events. ​Non-Blocking Remote Gateway: An asynchronous FreeRTOS background task pinned to Core 0 relays every command and glove-disconnection warning directly to the caregiver’s smartphone via a Telegram Bot, ensuring family members stay informed even when away from the base station without delaying real-time local alarms.
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Cover image for Title: SmartMill Guardian - CNC
Title: SmartMill Guardian - CNC Machine Predictive Health & Vibration Diagnostics Edge System Problem statement: High-precision CNC milling centers and industrial machining tools frequently suffer from sudden spindle bearing degradation and thermal runaway. A single unplanned spindle seizure costs manufacturing facilities upwards of $40,000 per incident in damaged workpieces, emergency tooling, and shop-floor downtime. Traditional plant maintenance relies on manual spot checks every 4 to 8 hours or cloud-only IoT gateways that suffer from 2–5 second network latencies—far too slow to trigger an emergency feed-hold before catastrophic mechanical failure. Furthermore, retrofitting older factory machinery with proprietary PLC hardware (e.g., Siemens S7 racks) is prohibitively expensive and requires disruptive rewiring. Solution: Designed and deployed a deterministic edge monitoring unit powered by a Raspberry Pi 4B running bare-metal FreeRTOS and Embedded C (C99): High-Rate Vibration FFT: Interfaces with an ADXL345 3-axis MEMS accelerometer via hardware SPI at 1 kHz ODR. An on-chip CMSIS-DSP Fast Fourier Transform (FFT) paired with a lightweight µTensor INT8 model identifies bearing defect frequencies (BPFO/BPFI harmonics) with 94% accuracy. Thermal Profiling & PLC Bridging: Continuously monitors bearing temperatures using an MLX90614 infrared sensor over I²C while polling spindle speed, drive current, and status words from existing Siemens S7-1200 PLCs over RS-485 Modbus RTU. Deterministic Emergency Interlock: Features a 4-channel opto-isolated relay interface capable of triggering a CNC machine feed-hold or spindle stop in <5 ms upon anomaly detection—eliminating cloud latency. Cloud & SCADA Telemetry: Securely streams time-series vibration metrics and alerts via TLS 1.3 MQTT to AWS IoT Core, populating live shop-floor InfluxDB and Grafana dashboards for condition-based maintenance scheduling.
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Designing PCBs, electronics, embedded and product design
New to Contra
Designing PCBs, electronics, embedded and product design
AI Campaign Assets + Agents for DTC Growth
    $100k+
    Earned
    21x
    Hired
    5.0
    Rating
    301
    Followers
AI Campaign Assets + Agents for DTC Growth
Brand designer & strategist
    2x
    Hired
    5.0
    Rating
    11
    Followers
Brand designer & strategist
Game Developer | Prototyping, Mobile, AI & Multiplayer
Game Developer | Prototyping, Mobile, AI & Multiplayer
Cover image for One conversation with a client
One conversation with a client reminded me that innovation isn't always about creating something from scratch. Sometimes, it's about taking a good product that's struggling to deliver the right experience - and helping it reach its full potential. Recently, a team approached us after developing an educational multiplayer game designed to create awareness around reducing carbon emissions. The concept, gameplay, and design were already in place. They weren't looking for a redesign. They wanted players to enjoy the experience as much as they believed in the idea. The Challenges We Identified - After reviewing the application, a few key issues stood out: - Frequent lag and game freezes were disrupting gameplay. - Animations running at 30 FPS made the experience feel less responsive. - Graphics were limited to 720p, affecting visual quality. - Players found parts of the interface confusing, making the game less intuitive. - Hindi graphics weren't rendering correctly, limiting accessibility. - Since the game required five participants, sessions often couldn't begin if enough players weren't available. None of these problems affected the concept of the game-but they had a significant impact on player engagement. How We Solved Them Instead of changing the product itself, we focused on improving how users experienced it. Our team: - Increased animation performance from 30 FPS to 60 FPS for smoother gameplay. - Upgraded the graphics from 720p to 1080p to create a sharper and more immersive experience. - Simplified the interface so players could understand the game more naturally. - Implemented Chanakya to render Hindi graphics accurately. - Introduced AI-powered players, allowing games to continue even when all five participants weren't available. The Outcome What changed wasn't the idea - it was the experience. The gameplay became smoother, the visuals became more engaging, multilingual support made the product more inclusive, and AI ensured players could enjoy uninterrupted sessions without waiting for a full group. For me, this project reinforced an important lesson: users rarely stop using a product because the idea is weak. More often, they leave because small technical friction gets in the way of a great experience. As technology partners, that's where we can make the biggest difference - not by replacing good ideas, but by helping them perform the way they were always meant to. I'm curious to know your perspective. If you had to prioritize just one while improving a digital product, what would it be? A) Performance B) User Experience C) Accessibility D) AI-powered capabilities Drop your answer in the comments - I’d love to know what you think.
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Cybersecurity Analyst | Cloud Security & IT Operations
New to Contra
Cybersecurity Analyst | Cloud Security & IT Operations
Cover image for This is a project I
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
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