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Found 8 papers, 0 papers with code
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems.
Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on common sequences of the grasp movements.
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels
Convolutional neural network-based computer vision control of the prosthetic hand has received increased attention as a method to replace or complement physiological signals due to its reliability by training visual information to predict the hand gesture.
HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands
Specifically, paired images from human eye-view and hand-view of various objects placed at different orientations have been captured at the initial state of grasping trials, followed by paired video, EMG and IMU from the arm of the human during a grasp, lift, put-down, and retract style trial structure.
NetCut: Real-Time DNN Inference Using Layer Removal
Deep Learning plays a significant role in assisting humans in many aspects of their lives.
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities.
