Search Results for "sensor data"
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Showing 194 open source projects for "sensor data"
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Adafruit Unified Sensor Driver
Common sensor library
Many small embedded systems exist to collect data from sensors, analyze the data, and either take appropriate action or send that sensor data to another system for processing. One of the many challenges of embedded systems design is the fact that parts you used today may be out of production tomorrow, or system requirements may change and you may need to choose a different sensor down the road. -
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kapture
Tools for manipulating datasets
Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion) and more generally sensor-acquired data. -
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Segments.ai
Segments.ai Python SDK
Multi-sensor labeling platform for robotics and autonomous vehicles. The platform for fast and accurate multi-sensor data annotation. Label in-house or with an external workforce. Intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Obtain segmentation labels, vector labels, and more. Our labeling interfaces are set up to label fast and precise. -
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CapFrameX
Frametime capture and analysis tool
CapFrameX is a performance capture and analysis tool designed for PC gamers and benchmarking enthusiasts, which records frametimes, frame-rates and various sensor data during game or application execution. It uses backend tools like PresentMon to log data and then exposes a comprehensive UI for analyzing the results: you can view charts of frametimes, historic graphing, stuttering analysis, L-shape graphs, input-lag overlays, and compare multiple capture runs side by side. Importantly, the tool also integrates with sensor inputs (CPU, GPU, VRAM, temps, etc.) and overlays statistics in-game via Rivatuner Statistics Server, so you get in-situ feedback while you run. ... -
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FanCtrl
FanCtrl allows you to automatically control the fan speed
...It combines monitoring and control in one place, letting you view temperatures, fan RPM, and control percentages while you tune how aggressively each fan responds. The project supports a range of control backends and integrations, including motherboard fan headers and several popular cooling ecosystems, and it can also pull sensor data from common monitoring tools so your fan logic can react to the readings you trust. A core concept is “auto fan control” profiles where you pick a target temperature sensor, select which fans should follow it, and then shape the curve via a graph-like interface. It also includes quality-of-life options like renaming items, choosing units (including Fahrenheit), and starting minimized for a set-it-and-forget-it setup. -
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Rerun
Visualize streams of multimodal data
Rerun is an open-source tool that helps developers visualize real-time multimodal data streams, such as images, point clouds, and tensors, for debugging and understanding ML and robotics systems. Designed for use with Python and Rust, it captures logged data and renders it through an interactive desktop interface, making it easier to understand how complex systems behave over time. -
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KCloud‑Platform‑IoT
KCloud-Platform-IoT
KCloud-Platform-IoT is a comprehensive open-source IoT management platform built with Spring Cloud and Vue.js. It supports device registration, data collection, rule-based processing, and dashboard visualization. Designed for scalability and modularity, the platform is ideal for managing large IoT fleets in industrial or smart city environments. -
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Open Hardware Monitor
Monitor temperature sensors, fan speeds, voltages, load & clock speeds
Open Hardware Monitor is a free and open-source hardware monitoring tool that helps users track the health and performance of their computer. It monitors key system metrics such as CPU temperatures, fan speeds, voltages, clock speeds, and system load. The software supports many hardware monitoring chips commonly found on modern motherboards. It can also read Intel and AMD processor temperature sensors, GPU sensors from ATI and Nvidia cards, and SMART hard drive temperatures. Users can view... -
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FarmBot OS
The operating system and all related software
...Communicate with the web application over WiFi or ethernet so that it can synchronize (download) sequences, regimens, farm designs, events, and more; upload logs and sensor data; and accept real-time commands. Communicate with the Farmduino to send G and F commands and receive sensor and encoder data. Take photos with a USB or Raspberry Pi camera, and upload the photos to the web application. You must use a .img writing tool to write FarmBot OS onto the microSD card. We recommend downloading and installing balenaEtcher for this purpose. -
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Alpamayo 1
Bridging Reasoning and Action Prediction
...The model is designed as a foundational component rather than a complete driving stack, allowing developers to build custom autonomous vehicle applications on top of it. It incorporates vision-language-action modeling, enabling it to process sensor data and contextual information simultaneously. Alpamayo supports tasks such as trajectory prediction, auto-labeling, and reasoning-based decision making. The system is optimized for high-performance GPU environments and is intended primarily for experimentation and benchmarking. Overall, it represents an advanced step toward integrating reasoning into autonomous driving pipelines. -
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OpenPlanter
Language-model investigation agent with a terminal UI
OpenPlanter is an open-source Python project focused on building an intelligent automated planting or gardening system powered by software control and data processing. The repository is designed to help developers and hobbyists create programmable plant management workflows that can monitor, schedule, and optimize growing conditions. It emphasizes automation and extensibility, allowing integration with sensors, environmental data, and control logic for smart cultivation setups. The system is... -
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Gyroflow
Video stabilization using gyroscope data
Gyroflow is an advanced open-source video stabilization application that uses gyroscope and motion sensor data to produce highly accurate and cinematic stabilization results. Instead of relying solely on visual estimation like traditional software stabilizers, it processes real motion data recorded by cameras or external sensors to achieve more precise compensation. This approach allows it to correct complex camera movement, rolling shutter distortion, and lens artifacts while preserving image quality. ... -
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Arduino core for the ESP32
Arduino core for the ESP32
...It features all the state-of-the-art characteristics of low-power chips, including fine-grained clock gating, multiple power modes,and dynamic power scaling. For instance, in a low-power IoT sensor hub application scenario, ESP32 is woken-up periodically and only when a specified condition is detected. The low-duty cycle is used to minimize the amount of energy that the chip expends. The output of the power amplifier is also adjustable, thus contributing to an optimal trade-off between communication range, data rate and power consumption. ... -
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immudb
Immutable database based on zero trust, SQL and Key-Value, tamperproof
immudb is a database with built-in cryptographic proof and verification. It tracks changes in sensitive data and the integrity of the history will be protected by the clients, without the need to trust the database. It can operate both as a key-value store, and/or as a relational database (SQL). Traditional database transactions and logs are mutable, and therefore there is no way to know for sure if your data has been compromised. immudb is immutable. You can add new versions of existing... -
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Trafexia
Mobile Traffic Interceptor
Trafexia is a lightweight web application designed to visualize real-time and historical transportation data, such as traffic flows, transit schedules, or route usage, with clean dashboards and map overlays that help users and analysts interpret mobility patterns quickly. It integrates data streams from APIs, CSV feeds, or sensor inputs and processes them into intuitive visualizations including flow diagrams, heatmaps, time series charts, and route performance boards. ... -
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Open Wearables
Self-hosted platform to unify wearable health data
Open Wearables is an open-source initiative that aims to provide a community-driven ecosystem for wearable device software and interoperability by connecting sensor data, activity tracking, and health insights across multiple platforms and devices. Instead of relying on closed vendor ecosystems, the project provides standardized data models and APIs that let developers and hobbyists collect, sync, and analyze biometric and environmental data from wearables, DIY sensors, and open hardware projects. ... -
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Seamless Communication
Foundational Models for State-of-the-Art Speech and Text Translation
...The motivation is to move beyond “text in, text out” and enable direct, live, multi-turn exchange involving language, gesture, gaze, vision, and modality switching without user friction. The system architecture includes a real-time multimodal signal pipeline for audio, video, and sensor data, a dialog manager that can decide when to act (speak, gesture, point) or query, and a cross-modal reasoning layer that fuses perception with semantic context. The research prototype includes components for visual grounding (understanding when a user references something in view), gesture recognition and synthesis, and turn-taking mechanisms that mirror human conversational timing. ... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write... -
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ImageBind
ImageBind One Embedding Space to Bind Them All
...The model is trained using large-scale contrastive learning, leveraging diverse datasets from natural images, videos, audio clips, and sensor data. Once trained, it can perform cross-modal retrieval, zero-shot classification, and multimodal composition without additional fine-tuning. -
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SLAM in Autonomous Driving Book
SLAM Technology in Autonomous Driving
SLAM in Autonomous Driving Book is the open-source code companion to the book SLAM in Autonomous Driving. It focuses on localization and mapping systems that combine LiDAR, IMU, GNSS, and vehicle motion data. The material begins with geometry, kinematics, Kalman filtering, inertial navigation, preintegration, and graph optimization. It then implements point-cloud processing, nearest-neighbor structures, ICP, NDT, scan matching, occupancy grids, and loop closure. Readers build both loosely... -
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PilottAI
Python framework for building scalable multi-agent systems
pilottai is an AI-based autonomous drone navigation system utilizing reinforcement learning for real-time decision-making. It is designed for simulating and training drones to fly safely through dynamic environments using AI-based controllers. -
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ESPectre
Motion detection system based on Wi-Fi spectre analysis (CSI)
ESPectre is an open-source motion detection system that transforms ordinary Wi-Fi signals into an ambient sensor capable of detecting movement without using cameras, microphones, or additional radio hardware, making it a privacy-preserving and cost-effective alternative for presence sensing and smart home automation. At its core, it analyzes Wi-Fi Channel State Information (CSI) — detailed measurements of how Wi-Fi waves change as they propagate — to mathematically detect disturbances caused by human movement between a Wi-Fi router and an ESP32 microcontroller, eliminating the need for machine learning or training data to operate effectively. ... -
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DriveLM
Driving with Graph Visual Question Answering
...The project introduces a new paradigm called graph visual question answering that structures reasoning about driving scenes through interconnected tasks such as perception, prediction, planning, and motion control. Instead of treating autonomous driving as a purely sensor-driven pipeline, DriveLM frames it as a reasoning problem where models answer structured questions about the environment to guide decision making. The system includes DriveLM-Data, a dataset built on driving environments such as nuScenes and CARLA, where human-written reasoning steps connect different layers of driving tasks. This design allows models to learn relationships between objects, behaviors, and navigation decisions through graph-structured logic. -
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FasterLIO
Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry
...It builds on FastLIO2 while replacing key spatial operations with parallel sparse incremental voxels. The design targets high update rates for both solid-state and spinning LiDAR sensors. It fuses LiDAR measurements with inertial data through an iterative Kalman-filter-based estimation pipeline. The software supports online ROS operation as well as offline processing of recorded bag files. Users can compile it with CMake or catkin, run it through Docker, visualize output in RViz, and save trajectories and point clouds. Configuration files support multiple sensor datasets, while selectable voxel implementations allow performance and stability tuning. -
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ICS Sensor Sim Lab
ICS Sensor Traffic Simulator
This tool simulates network traffic typical of an Industrial Control System (ICS) environment. It generates synthetic Modbus/TCP packets to help test monitoring and intrusion detection systems. Quick Start To use this tool, run the simulator: bash # Run for 30 seconds and save to traffic.log python lab_runner.py ics-sim --duration 30 --output traffic.log
