Related papers: Supervised Radio Frequency Interference Detection …

Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection…

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally…

We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…

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…

Spike sorting is a crucial step in decoding multichannel extracellular neural signals, enabling the identification of individual neuronal activity. A key challenge in brain-machine interfaces (BMIs) is achieving real-time, low-power spike…

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…

Spiking Neural Networks (SNNs) are emerging as a promising alternative to Artificial Neural Networks (ANNs) due to their inherent energy efficiency. Owing to the inherent sparsity in spike generation within SNNs, the in-depth analysis and…

In the frame of the Astronomical Validation activities for the 64m Sardinia Radio Telescope, we performed 5-22 GHz imaging observations of the complex-morphology supernova remnants (SNRs) W44 and IC443. We adopted innovative observing and…

In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and challenging inverse computation problem. In scenarios where…

As a new generation radio telescope, the Allen Telescope Array (ATA) is a prototype for the square kilometer array (SKA). Here we describe recently developed design constraints for the ATA digital signal processing chain as a case study for…

We present a new methodology to explore the morphology of the High Frequency Feature (HFF), i.e., the dominant, rising-frequency GW emission from a proto-neutron star in core-collapse supernovae (CCSNe). We used a residual neural network…

Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing. Among neural coding schemes of SNNs, Time-To-First-Spike (TTFS) coding, which encodes…

Observations of high-redshift supernovae (SNe) open a novel opportunity to study the massive star population in the early Universe. We study the detectability of superluminous SNe with upcoming optical and near-infrared (NIR) surveys. Our…

Spiking neural networks (SNNs) are becoming a promising alternative to conventional artificial neural networks (ANNs) due to their rich neural dynamics and the implementation of energy-efficient neuromorphic chips. However, the…

Radio-frequency interference detection and flagging is one of the most difficult and urgent problems in 21 cm Epoch of Reionization research. In this work, we present $\chi^2$ from redundant calibration as a novel method for RFI detection…

In the past decade, advances in Artificial Neural Networks (ANNs) have allowed them to perform extremely well for a wide range of tasks. In fact, they have reached human parity when performing image recognition, for example. Unfortunately,…

Hardware-based spiking neural networks (SNNs) are regarded as promising candidates for the cognitive computing system due to low power consumption and highly parallel operation. In this work, we train the SNN in which the firing time…

Spiking neural networks (SNNs) present a promising computing paradigm for neuromorphic processing of event-based sensor data. The resonate-and-fire (RF) neuron, in particular, appeals through its biological plausibility, complex dynamics,…

Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons, can be operated in an event-driven manner and have internal states to retain information over time, providing opportunities for energy-efficient neuromorphic…

Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs based on synaptic…