Related papers: Development of a Simulation Model and Precise Timi…
The state of the art in electromagnetic Finite Element Particle-in-Cell (EM-FEMPIC) has advanced significantly in the last few years. These have included understanding function spaces that must be used to represent sources and fields…
Large-scale systems, such as very large accelerators used for fundamental research, require the implementation of precise timing and synchronization systems over distances of several tens of kilometers. A very high precision has been…
Precision timing has alway been a prominent topic in the instrumentation for high-energy physics experiments. In this document I discuss what the perspectives are for the use of silicon sensors for a time-of-flight detector in a…
This paper describes the hardware, firmware and software systems used in data acquisition for the NA61/SHINE experiment at the CERN SPS accelerator. Special emphasis is given to the design parameters of the readout electronics for the 40m^3…
The Internal Conversion Electron SPectrometer In Coincidence Experiments (ICESPICE) demonstrator has been developed at Florida State University to enable particle/gamma-electron coincidence measurements in low-energy nuclear structure…
Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) are promising candidates for…
When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy concerns. This paper develops and investigates two…
Spike sorting is a valuable tool in understanding brain regions. It assigns detected spike waveforms to their origins, helping to research the mechanism of the human brain and the development of implantable brain-machine interfaces (iBMIs).…
The determination of the neutrino mass is an open issue in modern particle physics and astrophysics. The direct mass measurement is the only theory-unrelated experimental tool capable to probe such quantity. The HOLMES experiment aims to…
Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training high accuracy models. A major thread of research on SNNs is on…
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, based on which real-time predictions can be made locally…
A gaseous beam monitor utilizing gas electron multiplier (GEM) and pixel sensors is being developed for the Cooling Storage Ring (CSR) External-target Experiment (CEE) at Heavy Ion Research Facility in Lanzhou (HIRFL). The beam monitor is…
Energy detection is widely used for spectrum sensing, but accurately localizing the time and frequency occupation of signals in real-time for efficient spectrum sharing remains challenging. To address this challenge, we present RISE, a…
We report experimental test-beam results on dielectric-loaded waveguide detectors that utilize microwave Cherenkov signals to time and characterize high energy particle showers. These results are used to validate models and produce…
We present time-resolved X-ray diffraction measurements using advanced timing schemes that provide high temporal resolution while also maintaining a high flux in the X-ray probe beam. The method employs patterned probe pulse sequences that…
With the sensor scaling of next-generation Brain-Machine Interface (BMI) systems, the massive A/D conversion and analog multiplexing at the neural frontend poses a challenge in terms of power and data rates for wireless and implantable…
We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum likelihood inference in latent variable models. By combining…
In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be…
In this paper we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the Expectation Consistency (EC) framework,…
We introduce a wireless RF network concept for capturing sparse event-driven data from large populations of spatially distributed autonomous microsensors, possibly numbered in the thousands. Each sensor is assumed to be a microchip capable…