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Found 17 papers, 1 papers with code
On Models and Approaches for Human Vital Signs Extraction from Short Range Radar Signals
The paper centres on an assessment of the modelling approaches for the processing of signals in CW and FMCW radar-based systems for the detection of vital signs.
Non-invasive Diver Respiration Rate Monitoring in Hyperbaric Lifeboat Environments using Short-Range Radar
Further, evidence is provided that the radar-based approach yields a more accurate measure of respiration rate than an audio signal from a headset microphone.
A Novel Micro-Doppler Coherence Loss for Deep Learning Radar Applications
Deep learning techniques are subject to increasing adoption for a wide range of micro-Doppler applications, where predictions need to be made based on time-frequency signal representations.
Interference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network
A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented.
On Input Formats for Radar Micro-Doppler Signature Processing by Convolutional Neural Networks
Convolutional neural networks have often been proposed for processing radar Micro-Doppler signatures, most commonly with the goal of classifying the signals.
Robustness of Deep Neural Networks for Micro-Doppler Radar Classification
Both small temporal shifts and adversarial examples are a result of a model overfitting on features that do not generalize well.
Modelling of Networked Measuring Systems -- From White-Box Models to Data Based Approaches
This paradigm shift holds true in particular for the digital future of measurement in all spheres of our lives and the environment, where data provided by large and complex interconnected systems of sensors are to be analysed.
Exploring the Capability of Text-to-Image Diffusion Models with Structural Edge Guidance for Multi-Spectral Satellite Image Inpainting
The letter investigates the utility of text-to-image inpainting models for satellite image data.
Detecting Cloud Presence in Satellite Images Using the RGB-based CLIP Vision-Language Model
This work explores capabilities of the pre-trained CLIP vision-language model to identify satellite images affected by clouds.
Neural Weight Step Video Compression
A variety of compression methods based on encoding images as weights of a neural network have been recently proposed.
Neural Knitworks: Patched Neural Implicit Representation Networks
Coordinate-based Multilayer Perceptron (MLP) networks, despite being capable of learning neural implicit representations, are not performant for internal image synthesis applications.
Utilising Flow Aggregation to Classify Benign Imitating Attacks
The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour.
Leveraging Siamese Networks for One-Shot Intrusion Detection Model
Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to re-train the model for every unseen cyber-attack class.
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
Machine Learning Approach for Detection of nonTor Traffic
A study to compare the reliability and efficiency of Artificial Neural Network and Support vector machine in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this paper.
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles.
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey
Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works.
