AI Leak Detection for IoT

@Mahihassi

With extensive experience in embedded systems, firmware development, and PCB design, I am well positioned to deliver the comprehensive IoT solution you need for your smart monitoring project. My expertise in Microcontrollers including STM32, ESP32, TI Tiva, C2000, MSP430 and strong grasp of C/C++ and Python make me fluent to integrate with TensorFlow Lite and PyTorch as per your requirement. Moreover, I have hands-on proficiency in implementing AI on the edge with TensorFlow Lite and PyTorch. Since your data needs to be compatible with MQTT and HTTP for existing SCADA infrastructure, I can ensure smooth communication. My previous works have equipped me with the ability to process signals effectively and implement robust sensor-AI systems. This makes me well-suited for your project's need for ultrasonic flow sensor data processing. Also, I’m skilful at containerising software making sure it can run seamlessly on different platforms which are one of your requirements. Moreover, my end-to-end product development workflow starting from Idea -> Prototype -> Final Product aligns perfectly with the tasks mentioned in the project description.

@intelliresponse

Hello, I understand the challenge is not only detecting leaks from ultrasonic flow sensor streams, but also predicting failures early and converting noisy waveform data into actionable maintenance insights with low-latency deployment. I’ve worked with AI pipelines involving time-series/sensor processing, anomaly detection, forecasting models, and containerized deployment workflows. My approach would combine signal preprocessing (FFT/features + raw sequence analysis), anomaly detection for leak events, and predictive models for remaining useful life (RUL), exposed through an API with MQTT/HTTP integration to SCADA systems. I’d likely use Python with PyTorch/TensorFlow Lite, FastAPI, Docker, and lightweight dashboards (React or Streamlit), with optional edge deployment on Raspberry Pi/gateways depending on inference requirements. Deliverables would include reproducible training pipelines, inference APIs, deployment documentation, and cross-platform packaging designed for fast technician setup. I’d be glad to discuss your dataset, sensor specs, and hardware roadmap to recommend the most reliable architecture.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@shadabkhan92

As someone who specializes in building and deploying AI systems that work within existing workflows, your project aligns perfectly with my expertise. Having tackled tasks similar to yours in the past, I am fully equipped for this AI Leak Detection for IoT project. To truly detect leaks the moment they start and forecast failures before they happen, you need an experienced signal-processing adept like me in your corner. Utilizing Python or C++ will be ideal for integrating with TensorFlow Lite or PyTorch, which I have great knowledge of. Also, given our past successful integration of Odoo ERP end-to-end, into data-intensive ML models and sensor data reading pipelines, I understand the importance of minimal latency and bidding on multiple platforms to cater to a diverse system set up like yours. Additionally, my broad range of skills in C programming, Data Visualization, Machine Learning (ML), Python, and Microcontroller will be pivotal in ensuring I deliver not just a functioning model but a well-packaged and deployed software that can run across various operating systems. Plus, my experience with MQTT and HTTP will guarantee seamless integration with your SCADA stack. Together we can create a reproducible training pipeline that brings actionable analytics to the forefront via API with ease of use for your technicians' perusal in under 15 minutes.

@quan9824

As a full-stack developer with a rich history in algorithmic trading, web development, and AI systems, I believe I hold the complementary set of skills that your project demands. In particular, my extensive expertise in Time Series Forecasting and Machine Learning will ensure that your smart monitoring system detects leaks and predicts any failures well before they happen. I have a deep understanding of Python and C++, which are key for integrating with frameworks such as TensorFlow Lite and PyTorch. The ability to quickly adapt to new technologies and project requirements is another strength I can bring to the table. This is critical for your project, which requires compatibility across different platforms like Windows, Linux, and macOS. Lastly, my commitment lies in delivering top-tier services that align perfectly with the needs of my clients. I always focus on clean and maintainable code, essential for reproducible training pipelines as required by your project. With impressive job completion rates and prompt delivery times noted by my previous clients, you can count on me to provide a high-quality solution alongside thorough documentation for future technicians installing or flashing the software into your IoT system. Let's discuss further how we can integrate my skillset with your hardware roadmap!

@hamzazayyad

Dear Sir, I have strong experience in embedded systems, industrial sensing, signal processing, and AI-based monitoring platforms. I can develop your ultrasonic pipe-monitoring solution for real-time leak detection, predictive maintenance, and analytics with low-latency operation. I can work with ultrasonic waveform + flow data and build: • Leak detection models • Remaining-life/failure prediction • Real-time analytics dashboard • MQTT/HTTP integration with SCADA • Cross-platform deployment for Windows/Linux/macOS using Docker Proposed stack: • Python + PyTorch/TensorFlow Lite • NumPy/SciPy for DSP and feature extraction • FastAPI/Flask backend API • MQTT broker integration • Streamlit/Grafana-style dashboard • Dockerized deployment for easy installation I understand real-world ultrasonic sensing issues such as turbulence noise, cavitation, transient spikes, and sensor drift, so the system will be designed for practical industrial conditions rather than only lab data. Deliverables: • Reproducible training pipeline with documented source • Inference engine returning leak probability + remaining-life estimate • Lightweight dashboard with charts, alerts, and CSV export • Step-by-step deployment guide for fast installation I can also augment/simulate leak scenarios from your dataset to improve model robustness and help align the software architecture with your future hardware roadmap. Best regards, Hamza Electronics Engineer

@mobasherreza10

Hi there, Drawing from my multidisciplinary background and diverse skill set, I am confident in offering you a comprehensive solution for your AI Leak Detection project. My experience with both Python and C++, particularly in relation to handling data streams and integrating with frameworks like TensorFlow Lite and PyTorch, makes me the perfect candidate to determine whether running your model on edge or in the cloud is the most efficient solution for your needs. Lastly, my skills in DevOps and ability to create step-by-step deployment guides ensures efficient implementation of your software across different platforms without delays or cumbersome code changes. As you mention that real-time monitoring and visualization are crucial to your project, my proficiency with Grafana, Prometheus, Power BI, Tableau et al., guarantees you'll have access to lightweight web dashboards with insightful charts plus alert mechanisms. Rest assured that by choosing me you'll have not just AI capability but also an all-rounded technician!

@WishingBell

Hi, I can help build the leak detection, failure forecasting, and analytics system around your ultrasonic flow sensor data. I have experience with sensor-data processing, anomaly detection, time-series ML, edge/cloud inference, MQTT/HTTP integrations, Python/C++ services, Docker packaging, and lightweight dashboards for real-time monitoring. For this project, I would use a practical toolchain such as Python, NumPy/SciPy for signal preprocessing, PyTorch or TensorFlow Lite for model training/inference, FastAPI for the API layer, MQTT/HTTP for SCADA communication, Docker for cross-platform deployment, and React or Streamlit for the dashboard. My approach would be: ➡️Clean and analyze waveform/flow data ➡️Build a reproducible training pipeline ➡️Train models for leak probability and remaining-life estimation ➡️Package the inference engine for edge or cloud deployment ➡️Expose results through API/MQTT ➡️Build a dashboard with live charts, alerts, and CSV export ➡️Provide a technician-friendly deployment guide I can design this as a scalable, production-oriented system with low latency, clean documentation, and a deployment process simple enough for field technicians. I am looking forward to work with you. Kind regards. Andrew

@riogushiken1114

Hi there, I reviewed your AI Leak Detection for IoT project carefully, and I can help you build a low-latency sensor-AI system that detects leaks, predicts failures, and turns ultrasonic flow data into clear operational analytics. Why I’m a good fit: • Strong Python/C++ ML pipeline experience for time-series and waveform signal processing • Hands-on work with TensorFlow Lite/PyTorch, MQTT/HTTP APIs, Linux deployment, and containerised services • Practical focus on fast inference, reliable alerts, technician-friendly setup, and clean SCADA integration For this build, I would use Python for training/data processing, PyTorch or TensorFlow Lite for inference, C++/Python bindings where edge performance is needed, Docker for cross-platform deployment, MQTT/HTTP for SCADA, and a lightweight dashboard with real-time charts, alerts, and CSV export. My approach: • Reproducible training pipeline and documented source • Leak probability + remaining-life API • Deployment guide installable in under 15 minutes I can start immediately and would be happy to discuss the project in more detail. Best regards,

@Georgeoustar

Hi, I’ve worked on similar sensor-AI and industrial monitoring projects involving time-series data, signal processing, anomaly detection, predictive maintenance, MQTT/HTTP APIs, containerized deployment, and dashboards for real-time alerts. I also have experience building ML pipelines where raw sensor readings are cleaned, labelled, trained, validated, and deployed through lightweight inference services for edge or cloud environments. For your ultrasonic flow monitoring system, I can build a reproducible Python-based training pipeline using PyTorch or TensorFlow Lite, process waveform and flow data, train leak detection and remaining-life prediction models, and expose inference results through an API returning leak probability and failure-risk estimates. I can also package the service with Docker for Windows, Linux, and macOS, integrate MQTT or HTTP with your SCADA stack, and build a lightweight dashboard with live charts, alerts, and CSV export. Best regards, George

@jtmerch

Choosing me for your ultrasonic flow sensor monitoring platform means choosing a developer who is experienced across the full range of signal-processing AI, real-time anomaly detection, edge/cloud inference systems, and industrial telemetry integration necessary for this project. Whether it's building leak-detection models from waveform data, forecasting pipe degradation and failure risk, or integrating inference outputs into SCADA-compatible MQTT/HTTP pipelines, I have the skills to deliver a reliable and production-ready monitoring solution.

@Srashtasoft

We can develop this solution using Python/C++ with TensorFlow Lite or PyTorch, depending on your hardware roadmap. The system can run on edge devices such as Raspberry Pi/gateways or in the cloud while maintaining low-latency inference. We will deliver a complete training pipeline, inference API, MQTT/HTTP communication layer, cross-platform containerised deployment, and a real-time monitoring dashboard with alerts and CSV export. Our experience includes: =================== Real-time sensor data processing and anomaly detection Predictive maintenance and ML-based forecasting systems MQTT/SCADA integrations for industrial IoT environments Edge AI deployment on Linux-based gateways and embedded hardware Data visualization dashboards and analytics platforms A few questions before we proceed: ============================= What is the sensor sampling frequency and waveform format? Do you already have historical failure/leak events labeled in the dataset? Which edge hardware or gateway devices are planned for deployment? Does the SCADA stack currently support MQTT topics, REST APIs, or both? Best Regards, Srashtasoft Team

@smstaci

Hello I’ve been working with Python, C++, TensorFlow/PyTorch, time-series analytics, sensor-processing systems, MQTT integrations, edge AI deployments, industrial telemetry, and real-time monitoring platforms for over 7 years, and I enjoy building intelligent systems that transform raw sensor streams into actionable operational insights. I’m confident I can help you build a reliable leak-detection and predictive-monitoring platform around your ultrasonic flow sensors. Talk Soon, Pavlo.

@ivicab6

Hi, I’ve built systems that recognise anomalies and forecast failures in real-time, using a mix of edge and cloud processing. For instance, I’ve developed monitoring solutions for industrial machinery, integrating TensorFlow with Python to process sensor data and provide actionable insights. I can start with a small test task to align our requirements, such as creating a basic leak recognition model. Best Regards, Ivica

@MIndlabsAi6

Hey there, I'm Vishal Maharaj, a seasoned professional with 25 years of experience in C Programming, Python, Data Visualization, and Machine Learning, based in Perth, Australia. I understand the project involves developing an AI Leak Detection system for IoT devices using ultrasonic flow sensors. I would approach this project by leveraging my expertise in Python and TensorFlow Lite to create a robust model that can accurately detect leaks and forecast failures in real-time. If you're interested in discussing this further, feel free to initiate a chat. Cheers, Vishal Maharaj

@alluankit01

✨ I can help build the AI leak detection and predictive monitoring system for your ultrasonic flow sensor data, with a practical pipeline from raw waveform readings to real time alerts and analytics. My approach would be Python for the training pipeline, TensorFlow Lite or PyTorch for the model, FastAPI for the inference API, MQTT or HTTP for SCADA integration, and Docker packaging so it can run consistently on Windows, Linux, and macOS. For low latency, I would first evaluate whether edge inference on a Raspberry Pi or gateway is enough, then keep cloud processing optional for heavier analytics. The system would return leak probability, failure risk or remaining life estimate, and live sensor trends through a lightweight dashboard with alerts and CSV export. I can also work with your labelled dataset, augment leak scenarios where appropriate, and document the full training, inference, deployment, and technician setup process clearly. I have experience with Python, signal processing, sensor data workflows, ML pipelines, APIs, dashboards, and IoT style integrations, so I will focus on making the first version reliable, explainable, and easy to deploy in the field. Best regards Ankit

@exequiel12ifran

Hello, I have just read your job description carefully. I have experience working with Python, C++, TensorFlow, PyTorch, sensor-data processing, machine learning pipelines, MQTT/HTTP integrations, Linux environments, real-time analytics dashboards and containerized deployments across Windows, Linux and macOS. I have also worked on AI systems involving time-series analysis, anomaly detection, predictive maintenance and edge/cloud inference architectures. I am eager to work on this project as it perfectly fits to my current skills and experience. I am confident I can complete this project within a short timeframe. For this system, I would recommend a Python-based pipeline using PyTorch or TensorFlow Lite, Dockerized deployment, MQTT communication with SCADA, and a lightweight dashboard for real-time monitoring, alerts and CSV exports. I can also help augment the dataset and simulate leak/failure scenarios to improve model reliability. Looking forward to hearing from you. Kind Regards. Lautaro

@perfectwriter002

Hello client, I am excited to submit my proposal for your project. With years of experience in the field, I am confident in my ability to deliver high-quality work that meets your needs. I have carefully reviewed your project description and requirements; I understand that you are looking to achieve your project objectives. My approach will ensure that I deliver exactly what you have requested in the project. I will keep you updated on the project progress and ensure timely delivery. If you are interested in moving forward I’d be happy to discuss the project further and answer any questions you may have. Thanks for considering my proposal; I look forward to the opportunity to work with you. Please open your messenger and send me complete details to discuss it further. Thank you.

@norbertojrj

Hey there I saw your AI leak detection project for ultrasonic sensors and it looks like a really interesting IoT challenge. Ive built similar signal processing and ML systems before for water monitoring using Python C++ TensorFlow Lite and PyTorch both on edge devices like Raspberry Pi and in the cloud. Ill create a fast system that spots leaks right away predicts failures and turns sensor data into clear analytics with low latency. I can deliver the training pipeline inference API lightweight dashboard and a simple step-by-step guide so your team can install it easily on Windows Linux or macOS. Happy to use MQTT for your SCADA integration and work with the dataset you have. Let me know when youre ready to chat Ill share examples of my past sensor AI work and we can get started fast.