Related papers: Streaming readout for next generation electron sca…
A data acquisition (DAQ) system has been developed which will read out and control calorimeters serving as prototype systems for a future detector at an electron-positron linear collider. This is a modular, flexible and scalable DAQ system…
The TRISTAN detector is an upgrade to the KATRIN experiment to enable a differential measurement of the tritium $\beta$-decay spectrum to search for the experimental signature of keV scale sterile neutrinos. The TRISTAN detector upgrade…
At MAX IV pixelated area detectors are operated at high frame rates to take advantage of the X-ray beam properties available from the fourth generation synchrotron in scattering, diffraction and imaging applications. A variety of photon…
This work reports the performance evaluation of an SDR readout system based on the latest generation (Gen3) of the AMD's Radio Frequency System-on-Chip (RFSoC) processing platform, which integrates a full-stack processing system and a…
State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to…
In Phase II of the Project 8 neutrino mass experiment, electrons from the decays of tritium or ${}^{83\mathrm{m}}$Kr are detected via their $\approx$26 GHz cyclotron radiation while contained within a circular waveguide. The signal from a…
Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring, and sensor data classification in general. We propose a solution via Deep Delay…
Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this setting by proposing a novel task for multimodal video…
We demonstrate so-called repetitive non-destructive readout (RNDR) for the first time on a Single electron Sensitive Readout (SiSeRO) device. SiSeRO is a novel on-chip charge detector output stage for charge-coupled device (CCD) image…
Exponential increases in scientific experimental data are outstripping the rate of progress in silicon technology. As a result, heterogeneous combinations of architectures and process or device technologies are increasingly important to…
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc. However, in…
Given the rapid improvement of the detectors at high-energy physics experiments, the need for real-time data monitoring systems has become imperative. The significance of these systems lies in their ability to display experiment status,…
This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module…
The scheme of the data acquisition (DAQ) architecture in High Energy Physics (HEP) experiments consist of data transport from the front-end electronics (FEE) of the online detectors to the readout units (RU), which perform online processing…
The future Belle II experiment will employ a computer-farm based data reduction system for the readout of its innermost detector, a DEPFET-technology based silicon detector with pixel readout. A large fraction of the background hits can be…
This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can…
Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive…
Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to…
This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a…
We study the problem of spectrum sharing between goal-oriented (GO) and legacy data-oriented (DO) systems. For the former, data quality and representation is no longer optimized based on classical communication key performance indicators,…