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Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In…

The complexity of the Round Damped Detuned Structue (RDDS) for the JLC/NLC main linac is driven by the considerations of rf efficiency and dipole wakefield suppression. As a time and cost saving measure for the JLC/NLC, the dimensions of…

This paper presents the new generation of LabVIEW-based GPS receiver testbed that is based on National Instruments' (NI) LabVIEW (LV) platform in conjunction to C/C++ dynamic link libraries (DLL) used inside the platform for performance…

This paper presents a mixed-mode delay-locked loop (MM-DLL) with binary search (BS) locking, designed to cover a broad frequency range from 533 MHz to 4.26 GHz. The BS locking scheme optimizes the locking time, reducing it from a linear to…

The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment aims to detect coherent elastic neutrino-nucleus scattering from reactor antineutrinos using a dual-phase xenon time projection chamber. To validate the…

Photonic technologies offer great prospects for novel ultrafast, energy-efficient and hardware-friendly neuromorphic (brain-like) computing platforms. Moreover, neuromorphic photonic approaches based upon ubiquitous, technology-mature and…

The Short-Baseline Neutrino (SBN) Program is a short-baseline neutrino oscillation experiment in the Booster Neutrino Beam-line (BNB) at Fermilab. It consists of three Liquid Argon Time Projection Chambers (LArTPCs) from the Short-Baseline…

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory provides an intense, high-quality source of neutrinos from pion decay at rest. This source was recently used for the first measurements of coherent elastic…

The European Spallation Source (ESS) accelerator is composed of superconducting elliptical cavities. When the facility is running, the cavities are fed with electrical field from klystrons. Parameters of this field are monitored and…

Empirical potential structure refinement (EPSR) is a neutron scattering data analysis algorithm and a software package. It was developed by the British spallation neutron source (ISIS) Disordered Materials Group in 1980s, and aims to…

Spiking neural networks (SNNs), as one of the brain-inspired models, has spatio-temporal information processing capability, low power feature, and high biological plausibility. The effective spatio-temporal feature makes it suitable for…

Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training of SNNs remains an unresolved challenge. While…

Detector readout systems for medium to large scale physics experiments, and instruments in some other fields as well, are generally composed of multiple front-end digitizer boards distributed over a certain area. Often, this hardware has to…

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…

Entanglement generation between remote qubit systems is the central tasks for quantum communication. Future quantum networks will have to be compatible with low-loss telecom bands and operate with large separation between qubit nodes.…

The current SLC control system was designed and constructed over 20 years ago. Many of the technologies on which it was based are obsolete and difficult to maintain. The VMS system that forms the core of the Control System is still robust…

Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network…

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological…

High-Level Synthesis (HLS) is emerging as a mainstream design methodology, allowing software designers to enjoy the benefits of a hardware implementation. Significant work has led to effective compilers that produce high-quality hardware…

Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks. With the growing size and complexity of these networks, hardware implementation becomes…