The IEEE 5G/6G Innovation Testbed is a cloud-based, end-to-end 5G network emulator that enables testing and experimentation of 5G products and services. Secure, easily-accessible and “always on,” this platform brings 5G network testing and development to your fingertips and paves the way for speedier and smoother real world deployments. Learn more.
This dataset supports the evaluation of the Cooperative Greedy Response Algorithm for Collision Avoidance (CGRA-CA) in NR-V2X networks. The data include real-world traffic-flow measurements collected from urban intersections and expressways in Fuzhou, China, during peak traffic hours. Parameters such as vehicle counts, trajectories, and signal timing are included.
Electric Vehicle Charging Station Usage. Datasets for Perth & Kinross Council's EV charging stations under the ChargePlace Scotland scheme. Includes anonymous data from each individual charging session.
Electric Vehicle Charging Station Usage. Datasets for Perth & Kinross Council's EV charging stations under the ChargePlace Scotland scheme. Includes anonymous data from each individual charging session.
The blockage of propagation paths between the base station and user equipment, as well as micromobility due to fast rotation of the UE in the user's hands, are known to be the main phenomena affecting the performance of 6 G (sub-)terahertz cellular systems. Practical measurements of the received signal power (RSP) are generally limited to these phenomena in isolation. In this dataset, we provide time-series of the RSP, simultaneously capturing blockage, micromobility, and beamtracking.
Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the channel state information. Current channel estimation neural networks are frequently tested by training and testing on one example channel or similar channels. However, data-driven methods often degrade on new data which they are not trained on, because they cannot extrapolate their training knowledge.
This dataset consists of a small collection of real-time human face images and google source based human facial images captured in diverse environments and under varying lighting conditions. The dataset aims to provide a comprehensive resource for research in facial recognition, computer vision, and machine learning applications. The images include faces of individuals from different age groups, genders, and ethnicities, offering a varied representation of human features.
This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance.
The PdM_telemetry Dataset (D_1) is a synthetic dataset designed to support predictive maintenance (PdM) research for IIoT (Industrial Internet of Things) devices by providing sensor-based telemetry data. This dataset initially comprises 97,210 records and 30 features, including a binary target feature, 'failure', which indicates whether a device will fail within the next 24 hours. The remaining features, such as device operational metrics and error counts, serve as predictors.
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CAMARA is an open-source project which is part of Linux Foundation. It aims at defining service APIs by combining network APIs, over an operator domain. CAMARA works in close collaboration with the GSMA Operator Platform Group to align API requirements and definitions, and to publish APIs.
IEEE 5G testbed supports the CAMARA initiative and provides these APIs for data privacy and regulatory requirements and facilitate application to network integration.
CAMARA APIs allow to tackle several types of business use cases leveraging 4G/5G network capabilities.
The dataset consists of satellite optical images, landslide boundary shapefiles, and digital elevation models. It includes 770 landslide samples, comprising rockfalls, rockslides, and some debris landslides, along with 2003 negative samples covering various backgrounds. These samples were cropped from TripleSat satellite images taken between May and August 2018, with an image resolution of 0.8 meters.
