Related papers: Deep residual detection of radio frequency interfe…
The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this…
Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the…
Radio interferometric observations are less susceptible to radio frequency interference (RFI) than single dish observations. This is primarily due to : (1)fringe-frequency averaging at the correlator output and (2) bandwidth decorrelation…
A method of radio frequency interference (RFI) suppression in radio astronomy spectral observations is described based on the analysis of the probability distribution of an instantaneous spectrum. This method allows the separation of the…
Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems.…
We present a deep neural network-enabled method to accelerate near-field (NF) antenna measurement. We develop a Near-field Super-resolution Network (NFS-Net) to reconstruct significantly undersampled near-field data as high-resolution data,…
Accurate and high-resolution precipitation nowcasting from radar echo sequences is crucial for disaster mitigation and economic planning, yet it remains a significant challenge. Key difficulties include modeling complex multi-scale…
Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale…
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and…
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of…
Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of free parameters from their low-rate discrete samples. This task is often translated into a…
We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency…
We present a project to implement a national common strategy for the mitigation of the steadily deteriorating Radio Frequency Interference (RFI) situation at the Italian radio telescopes. The project involves the Medicina, Noto, and…
As of late an AI based free programming device has made it simple to make authentic face swaps in recordings that leaves barely any hints of control, in what are known as "deepfake" recordings. Situations where these genuine istic…
Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely…
Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Conventional diagnostic methods, reliant on clinical expertise and…
Radio frequency interference (RFI) already limits the sensitivity of existing radio telescopes in several frequency bands and may prove to be an even greater obstacle for future generation instruments to overcome. I aim to create a…
The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel…
In this paper, we present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN). Interference in 5G networks challenges high-quality service due to dense user equipment…
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike…