143 citations · 605 across the 25 of their papers we have counts for
25 papers
CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes
Yuan Fang, Fangzhan Shi, Xijia Wei +3
As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. Whil…
Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications
Robin Forsling, Simon J. Julier, Gustaf Hendeby
Ensuring sufficiently accurate models is crucial in target tracking systems. If the assumed models deviate too much from the truth, the tracking performance might be severely degra…
Gaussianity and the Kalman Filter: A Simple Yet Complicated Relationship
Jeffrey Uhlmann, Simon Julier
One of the most common misconceptions made about the Kalman filter when applied to linear systems is that it requires an assumption that all error and noise processes are Gaussian.…
Kalman Filter Auto-tuning through Enforcing Chi-Squared Normalized Error Distributions with Bayesian Optimization
Zhaozhong Chen, Harel Biggie, Nisar Ahmed +2
The nonlinear and stochastic relationship between noise covariance parameter values and state estimator performance makes optimal filter tuning a very challenging problem. Popular…
Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review
Katherine Wang, Simon Julier, Youngjun Cho
With the rising prevalence of autism diagnoses, it is essential for research to understand how to leverage technology to support the diverse nature of autistic traits. While tradit…
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
Chong Tang, Wenda Li, Shelly Vishwakarma +3
Motion tracking systems based on optical sensors typically often suffer from issues, such as poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns.…
Time Dependence in Kalman Filter Tuning
Zhaozhong Chen, Christoffer Heckman, Simon Julier +1
In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional app…
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
Chong Tang, Wenda Li, Shelly Vishwakarma +3
Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterp…
Gradient-Based Interpretability Methods and Binarized Neural Networks
Amy Widdicombe, Simon J. Julier
Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms. However, the effectiveness of interpreta…
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
Chong Tang, Wenda Li, Shelly Vishwakarma +3
Micro-Doppler analysis has become increasingly popular in recent years owning to the ability of the technique to enhance classification strategies. Applications include recognising…
Kalman Filter Tuning with Bayesian Optimization
Zhaozhong Chen, Nisar Ahmed, Simon Julier +1
Many state estimation algorithms must be tuned given the state space process and observation models, the process and observation noise parameters must be chosen. Conventional tunin…
Federated Learning for Coalition Operations
D. Verma, S. Calo, S. Witherspoon +8
Machine Learning in coalition settings requires combining insights available from data assets and knowledge repositories distributed across multiple coalition partners. In tactical…
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning
Moustafa Alzantot, Amy Widdicombe, Simon Julier +1
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these mod…
Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging
Youngjun Cho, Nadia Bianchi-Berthouze, Manuel Oliveira +2
Automatically monitoring and quantifying stress-induced thermal dynamic information in real-world settings is an extremely important but challenging problem. In this paper, we expl…
Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging
Youngjun Cho, Simon J. Julier, Nadia Bianchi-Berthouze
Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. A smartphone camera-based PhotoPlethysmoGraphy (PPG) and a low-cost…
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Steve McGuire, Christoffer Heckman, Daniel Szafir +2
Determining extrinsic calibration parameters is a necessity in any robotic system composed of actuators and cameras. Once a system is outside the lab environment, parameters must b…
Federated AI for building AI Solutions across Multiple Agencies
Dinesh Verma, Simon Julier, Greg Cirincione
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. Regulatory restric…
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Zhaozhong Chen, Christoffer Heckman, Simon Julier +1
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time an…
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
Youngjun Cho, Nadia Bianchi-Berthouze, Nicolai Marquardt +1
We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their pro…
Fusion of finite set distributions: Pointwise consistency and global cardinality
Murat Üney, Jérémie Houssineau, Emmanuel Delande +2
A recent trend in distributed multi-sensor fusion is to use random finite set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponentia…
ThermSense: Smartphone-based Breathing Sensing Platform using Noncontact Low-Cost Thermal Camera
Youngjun Cho, Nadia Bianchi-Berthouze, Simon J. Julier +1
The ability of sensing breathing is becoming an increasingly important function for technology that aims at supporting both psychological and physical wellbeing. We demonstrate The…
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings
Youngjun Cho, Nadia Bianchi-Berthouze, Simon J. Julier
We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost th…
Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging
Youngjun Cho, Simon J. Julier, Nicolai Marquardt +1
The ability to monitor respiratory rate is extremely important for medical treatment, healthcare and fitness sectors. In many situations, mobile methods, which allow users to under…
Unscented Orientation Estimation Based on the Bingham Distribution
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier +1
Orientation estimation for 3D objects is a common problem that is usually tackled with traditional nonlinear filtering techniques such as the extended Kalman filter (EKF) or the un…