Search Results for author:
Found 8 papers, 1 papers with code
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way.
Time Dependence in Kalman Filter Tuning
First, in theory, many of these measures do not guarantee a unique solution due to observability issues.
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet.
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms.
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning
When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model.
Federated AI for building AI Solutions across Multiple Agencies
The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult.
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems.
Structured Prediction of Unobserved Voxels From a Single Depth Image
Building a complete 3D model of a scene, given only a single depth image, is underconstrained.
