Related papers: ML-based Real-Time Control at the Edge: An Approac…
Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems directly from raw data. For time-varying systems the…
Accurate and fast performance prediction for dataflow-based accelerators is vital for efficient hardware design and design space exploration, yet existing methods struggle to generalize across architectures, applications, and…
Anomaly detection is increasingly important to handle the amount of sensor data in Edge and Fog environments, Smart Cities, as well as in Industry 4.0. To ensure good results, the utilized ML models need to be updated periodically to adapt…
In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more…
Efficient and timely calculations of Machine Learning (ML) algorithms are essential for emerging technologies like autonomous driving, the Internet of Things (IoT), and edge computing. One of the primary ML algorithms used in such systems…
Current developments of high-speed magnetic levitation technology using the principle of the electromagnet suspension (EMS) focus on reaching vehicle speeds of more than 600 km/h. With increasing vehicle speeds, however, updated control…
The Centre for the Clinical Application of Particles' Laser-hybrid Accelerator for Radiobiological Applications (LhARA) facility is being studied and requires simulation of novel accelerator components (such as the Gabor lens capture…
Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational…
Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such…
Achieving high-quality electron beams from laser-plasma accelerators critically relies on density tailoring to control electron dynamics during injection, acceleration, and extraction. We report on the experimental observation of electron…
Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensive both in terms of compute and memory requirements,…
With electron beam durations down to femtoseconds and sub-femtoseconds achievable in current state-of-the-art accelerators, longitudinal bunch length diagnostics with resolution at the attosecond level are required. In this paper, we…
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science,…
On board monitoring of the alertness level of an automotive driver has been a challenging research in transportation safety and management. In this paper, we propose a robust real time embedded platform to monitor the loss of attention of…
A real-time beam profile monitoring system is proposed for GeV photon beams at the BM4 beamline of the Mikamine site, Research Center for Accelerator and Radioisotope Science (RARiS; previously known as ELPH) at Tohoku University. This…
Plasma wakefield acceleration holds remarkable promise for future advanced accelerators. The design and optimization of plasma-based accelerators typically require particle-in-cell simulations, which can be computationally intensive and…
Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are communication and computation intensive, which demands for the…
This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that…
Machine learning models deployed on edge devices have enabled numerous exciting new applications, such as humanoid robots, AR glasses, and autonomous vehicles. However, the computing resources available on these edge devices are not…
Collimation systems are essential in particle accelerators to safely and efficiently manage unavoidable beam losses during operation. These systems rely on collimators, which are specially designed movable jaws or absorbers positioned close…