Related papers: Radio Frequency Interference Detection Using Effic…

This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver…

Accurate detection of retinal vessels plays a critical role in reflecting a wide range of health status indicators in the clinical diagnosis of ocular diseases. Recently, advances in deep learning have led to a surge in retinal vessel…

Detecting the redshifted global 21-cm signal from the cosmic dawn (CD) remains a major challenge due to strong terrestrial Radio Frequency Interference (RFI), particularly dominated by Frequency Modulation (FM) transmissions in the 88-110…

Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of…

The Search for Extraterrestrial Intelligence (SETI) attempts to address the possibility of the presence of technological civilizations beyond the Earth. Benefiting from high sensitivity, large sky coverage, an innovative feed cabin for…

RFI mitigation is a critically important issue in radio astronomy using existing instruments as well as in the development of next-generation radio telescopes, such as the Square Kilometer Array (SKA). Most designs for the SKA involve…

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level…

The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual…

The "search for extraterrestrial intelligence" (SETI) commensal surveys aim to scan the sky to find possible technosignatures from the extraterrestrial intelligence (ETI). The mitigation of radio frequency interference (RFI) is an important…

The Murchison Widefield Array (MWA) is a new low-frequency interferometric radio telescope built in Western Australia at one of the locations of the future Square Kilometre Array (SKA). We describe the automated radio-frequency interference…

The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at…

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on the transmitter hardware impairments. In this paper, we propose a scalable and robust RFFI framework achieved by deep learning powered…

In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms possess inherent limitations that cannot be overlooked.…

Computer vision researchers have extensively worked on fundamental infrared visual recognition for the past few decades. Among various approaches, deep learning has emerged as the most promising candidate. However, Infrared Small Object…

Ground-based 21-cm experiments targeting the global signal from the periods of Cosmic Dawn (CD) and Epoch of Reionization (EoR) are susceptible to adverse effects presented by i) the ionosphere ii) antenna chromaticity induced by objects in…

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf…

Detecting manipulated media has now become a pressing issue with the recent rise of deepfakes. Most existing approaches fail to generalize across diverse datasets and generation techniques. We thus propose a novel ensemble framework,…

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…

We investigated the use of a U-Net convolutional neural network for denoising simulated medium-resolution spectroscopic observations of stars. Simulated spectra were generated under realistic observational conditions resembling the Subaru…

Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results. This work delves into the understudied aspect of receptive field (RF) size and its impact on the U-Net and Attention…