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Found 10 papers, 1 papers with code
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
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.
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
Neural Style Transfer Enhanced Training Support For Human Activity Recognition
Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise.
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.
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition
We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters.
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.
Sparsity Based Autoencoders for Denoising Cluttered Radar Signatures
A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating wall clutter in indoor radar images.
Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System
We present an ultra-short range IEEE 802. 11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble.
