22 citations · 59 across the 10 of their papers we have counts for
11 papers
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values
Anand Dubey, Niall Lyons, Avik Santra +1
Human activity recognition (HAR) using IMU sensors, namely accelerometer and gyroscope, has several applications in smart homes, healthcare and human-machine interface systems. In…
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey +2
Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of n…
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking
Julius Ott, Lorenzo Servadei, Gianfranco Mauro +3
Nowadays, Deep Learning (DL) methods often overcome the limitations of traditional signal processing approaches. Nevertheless, DL methods are barely applied in real-life applicatio…
Utilizing Explainable AI for improving the Performance of Neural Networks
Huawei Sun, Lorenzo Servadei, Hao Feng +3
Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have…
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Julius Ott, Lorenzo Servadei, Jose Arjona-Medina +7
Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve…
Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation
Michael Stephan, Thomas Stadelmayer, Avik Santra +3
This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from…
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition
Souvik Hazra, Hao Feng, Gamze Naz Kiprit +5
Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic…
Light-weight Gesture Sensing Using FMCW Radar Time Series Data
Thomas Stadelmayer, Avik Santra, Robert Weigel +1
The paper proposes a novel feature extraction approach for FMCW radar systems in the field of short-range gesture sensing. A light-weight processing is proposed which reduces a ser…
A Matched-filter based method in the Synthetic Aperture Radar Images Using FMCW radar
Moein Movafagh, Avik Santra, Daniel Oloumi
The stretch processing architecture is commonly used for frequency modulated continuous wave (FMCW) radar due to its inexpensive hardware, low sampling rate, and simple architectur…
Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
Lorenzo Servadei, Huawei Sun, Julius Ott +6
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage o…
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19
Manu Siddhartha, Avik Santra
Background and Objective:Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3…