iot
Projects with this topic
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Custom firmware for my Anet A8 printer. Forked from https://github.com/MarlinFirmware/Marlin
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Smart Parking with a texture based identification algorithm: Local Binary Patterns (LBP).
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An air quality sensor which monitors air quality in a workspace, together with a history graph
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QGIS plugin for routing garbage trucks.
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This project develops video analytics towards anomaly detection on edge for smart cities under low light conditions: https://iotgarage.net/projects/VideoAnalyticstowardsAnomalyDetectionontheEdge.html
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In this project, we developed AnoML, which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, and deployment to the edge, fog, and cloud platforms with minimal user interaction: https://doi.org/10.1016/j.iot.2021.100437
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ELK + Arduino (MQ-2 and DHT11) for monitoring humidity, temperature and gas.
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ARM Fault handling functions for integrating into an STM32 HAL project. Derived from the MbedOS Fault Handler.
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Control unit my boiler and thermostat
Forked from https://github.com/proddy/EMS-ESP
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This project combines our own and well-known camera trap image classification pre-trained models under one roof. It integrates them via an ensemble model to increase the trustworthiness of the classification decision.
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This is a variant of our CamTrap.AI project (https://gitlab.com/IOTGarage/camtrap.ai). Our original CamTrap.AI system was designed to handle around 1,000 images at a time. CamTrap.AI Bulk Classifier is specifically developed to efficiently classify over 2 million images captured by DGFC (https://www.dgfc.life/) over a decade
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Thie project benchmarks various camera trap image classification models
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recipes for mraa : https://projects.eclipse.org/proposals/eclipse-mraa
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This is the accompanying code repository for microcontroller tutorials presented in the AWS IoT EduKit program using the M5Stack Core2 for AWS IoT EduKit reference Hardware.
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This project created a dataset that captures cyber data (Network Traffic) and physical data (sensor data from primitive sensors such as temperature, humidity, motion, etc.) from a smart home with the aim of detecting complex cyber-physical anomalies. The dataset can be found here: https://dx.doi.org/10.21227/sez1-2928
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In this project, we developed AnoML, which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, and deployment to the edge, fog, and cloud platforms with minimal user interaction: https://doi.org/10.1016/j.iot.2021.100437
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