Search Results for "path-movement"

RuView is an edge AI perception system that transforms ordinary WiFi signals into real-time environmental sensing and human pose estimation. Built on the concept of WiFi DensePose, it analyzes disturbances in WiFi Channel State Information (CSI) caused by human movement to reconstruct body position, breathing patterns, heart rate, and presence. Unlike traditional vision systems, RuView operates without cameras, wearables, or cloud connectivity, making it a privacy-first sensing solution. The system runs on low-cost hardware such as ESP32 sensor meshes and performs signal processing and machine learning directly at the edge. ...

...The project demonstrates how commodity mesh routers and signal processing techniques can be leveraged to infer dense human pose information, even through obstacles such as walls. It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. The repository includes components for data processing, model inference, and real-time visualization, making it suitable for research and experimental deployments. Its architecture emphasizes performance and reproducibility, allowing developers to explore non-visual motion capture systems using accessible hardware. ...

AQA-AODV is a new QoS routing protocol for MANETs based on AODV, which creates routes according to application QoS requirements. We have introduced link and path available bandwidth estimation mechanisms and an adaptive scheme that can provide feedback to the source node about the current network state, to allow the application to appropriately adjust the transmission rate. In the same way, we propose a route recovery approach into the AQA-AODV protocol, which provides a mechanism to detect the link failures in a route and re-establish the connections taking into account the conditions of QoS that have been established during the previous route discovery phase.