We present ‘Revelio’, a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels, ensuring reliable decoding by standard smartphone cameras. The decoder, driven by a two-stage neural network, uses a weighted differential accumulator for precise frame detection and symbol recognition. Initial experiments demonstrate Revelio’s effectiveness in interactive television, offering an unobtrusive method for meta-information transmission.

This paper introduces Text2Net, an innovative system designed to transform plain English descriptions into dynamic, interactive network simulations within the Emulator Virtual Engine–Next Generation (Eve-NG) environment. By integrating SOTA technologies from Natural Language Processing (NLP), Large Language Models (LLMs), and proposed adaptor software, Text2Net bridges the technical knowledge gap, enabling both technical and non-technical users to effortlessly create and interact with complex network topologies within a simulation environment. The system architecture combines an intuitive chat interface, GPT4 LLM to interpret user inputs, NLP key-value extraction, a simulation adaptor, and the EVE-NG engine. TextNet democratizes network emulation, empowering educators to efficiently construct simulations for interactive learning. It also benefits industrial prototyping and testing configurations.CCS CONCEPTS•Applied computing → Interactive learning environments; Computer-assisted instruction; IT architectures;•Networks → Network design principles; Programming interfaces; Topology analysis and generation; Logical / virtual topologies; Network manageability; Programmable networks; Network management; Network monitoring.

In this work, we propose a novel re-use of neuromorphic (event) cameras for joint sensing and communications. Event cameras work on the principle of capturing changes in the light intensities, essentially capturing events that lead to such changes. This makes them operable at low power and sample events at fast rates (equivalent to about 40K frames-per-second compared to RGB cameras). We propose a system design to leverage the time-sampling nature of events for optical wireless communication and the ability to sample a collective area of physical space for imaging. In particular, we propose to address the challenges to achieve passive optical wireless (backscatter) communication as well as computer vision functions such as object and path detection using a single neuromorphic camera device. We posit that such an integrated functioning through a single low-power device opens new avenues for visible/invisible light communication and visual scene processing.CCS CONCEPTS•Networks → Mobile networks;•Hardware → Signal processing systems;•Computing methodologies → Computer vision.

The goal of this research is to model the spatio-temporal dependencies of radon gas generation and movement underground. In this regard, we have embarked upon an interdisciplinary research effort that involves studying the dependencies of radon gas emanation with soil and environmental parameters. To this end we design, implement and deploy an innovative real-time sensor network, OpenRadon Lab, to develop a radon prediction model that maps its distribution along space and time. This network constitutes a soil-to-cloud wireless computing framework to enable machine learning assisted soil radon prediction, and optimized data offloading to conserve computing resources on the sensing devices.