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Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and…

In current generation digital phase locked loop (DPLL) architectures, techniques like adaptive loop bandwidth with loop order switching and switched phase-detection are employed to achieve better lock time and jitter performance. This work…

SND@LHC is a compact and stand-alone experiment designed to perform measurements with neutrinos produced at the LHC in the pseudo-rapidity region of ${7.2 < \eta < 8.4}$. The experiment is located 480 m downstream of the ATLAS interaction…

Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation, making them ideal for battery-powered intelligent devices. This paper presents a circuit-level memristive spiking neural…

MAC layer protocol design in a WSN is crucial due to the limitations on processing capacities and power of wireless sensors. The latest version of the IEEE 802.15.4, referenced to as IEEE 802.15.4e, was released by IEEE and outlines the…

Due to the nonlinear distortion in Orthogonal frequency division multiplexing (OFDM) systems, the timing synchronization (TS) performance is inevitably degraded at the receiver. To relieve this issue, an extreme learning machine (ELM)-based…

Neutron multiplicity counting (NMC) underpins plutonium assay in nuclear safeguards, arms control, and disarmament verification, but existing simulation tools are essentially limited to MCNPX-PoliMi [1] (export-controlled, MCNP license…

We propose hardware-oriented models of intrinsic plasticity (IP) and synaptic plasticity (SP) for spiking randomly connected recursive neural network (RNN). Although the potential of RNNs for temporal data processing has been demonstrated,…

Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irregularly sampled data triggered by events generated in…

Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven…

On December 15th the Swiss Light Source (SLS) produced a stored beam for the first time. This important milestone was achieved in a very tight time schedule. The fact that all major systems are controlled by Epics made this challenge…

Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high energy efficiency. However, some factors such as temporal…

Spiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with current direct training approaches are constrained to a…

With three complexes spread evenly across the Earth, NASA's Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising…

Baikal-GVD is a gigaton-scale neutrino observatory under construction in Lake Baikal. It currently produces about 100 GB of data every day. For their automatic processing, the Baikal Analysis and Reconstruction software (BARS) was…

Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficiency, multi-sample speculative decoding has been overlooked…

The innermost part of the ATLAS experiment will be a pixel detector containing around 1750 individual detector modules. A detector control system (DCS) is required to handle thousands of I/O channels with varying characteristics. The main…

This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework integrates a directly trained Spiking Neuron Network (SNN) regression model and…

Low-cost FPGA platforms can broaden access to neuromorphic systems research, but current spiking neural network (SNN) workflows remain divided between hardware-first implementations, which are difficult to integrate with PyTorch-style…

Time series analysis and modelling constitute a crucial research area. Traditional artificial neural networks struggle with complex, non-stationary time series data due to high computational complexity, limited ability to capture temporal…