Surface electromyography (EMG) can be used to interact with and control robots via intent recognition. However, most machine learning algorithms used to decode EMG signals have been trained on small datasets with limited subjects, impacting their generalization across different users and tasks. Here we developed EMGNet, a large-scale dataset for EMG neural decoding of human movements. EMGNet combines 7 open-source datasets with processed EMG signals for 132 healthy subjects (152 GB total size).

DALHOUSIE NIMS LAB BENIGN DATASET 2024-2 dataset comprises data captured from Consumer IoT devices, depicting three primary real-life states (Power-up, Idle, and Active) experienced by everyday users. Our setup focuses on capturing realistic data through these states, providing a comprehensive understanding of Consumer IoT devices.

The dataset comprises of nine popular IoT devices namely 

Amcrest Camera

Smarter Coffeemaker

Ring Doorbell

Amazon Echodot

Google Nestcam

Google Nestmini

Kasa Powerstrip

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models.