Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Low latency”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

207 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Hyperbaric Oxygen (HBO) on Awakening of Coma Patients with Traumatic Brain Injury

Coma caused by craniocerebral injury is a common condition of neurosurgical acute injury. There is no specific method to promote awakening in a clinic. Early comprehensive treatment may be helpful to patients. The common methods are hyperbaric oxygen (HBO) and low-frequency repetitive transcranial magnetic stimulation (rTMS). However, the application effect and mechanism of rTMS combined with HBO on coma patients with traumatic brain injury need to be further studied. The brain stem auditory evoked potential (BAEP) is examined by the Kennedy coma recovery scale (CRS-R), the recovery of brain function and the state of consciousness are evaluated, and the therapeutic effect is evaluated by the Glasgow Coma Scale (GCS). Cerebrospinal fluid NE level, MCA blood flow velocity, and left brainstem and right brainstem auditory evoked potential are used to evaluate brain rehabilitation. RTMS combined with HBO could shorten the wake-up time, improve the wake-up rate, improve the GCS score and CRS-R score, shorten the brain wave latency time of the left and right brainstem, increase the NE level of cerebrospinal fluid, and decrease the blood flow velocity of MCA. RTMS combines with HBO can improve the nerve excitability of brain cells, reduce the disturbance of consciousness, promote the functional recovery of brain injury, and has a certain role in promoting the awakening of patients with traumatic brain injury coma.

Liu, Mei↗
DOE OSTI2026

Digital Grid Twin–Direct Communication Scheme Test Bed for Assessing Relay-to-Relay Radio Antenna and Optical Fiber Performance and Misoperations

This study introduces a novel “Digital Grid Twin–Direct Communication Scheme” test bed. This advanced platform evaluates point-to-point communication between transmitter and receiver relays with optical fiber and radio omnidirectional antenna systems, implemented at the Advanced Protection lab in the Grid Research Innovation and Development Center at Oak Ridge National Laboratory. The increased diversity of energy sources has led to more protective relay misoperations. In North America, microgrid protection schemes now use point-to-point communication along distribution lines between relays to implement advanced logic in nonradial grids that include both high- and low-inertia generators. This trend challenges utilities to minimize misoperations while ensuring rapid fault clearance and accurate selectivity coordination between primary and backup relays. This study assesses relay-to-relay communication schemes by introducing an advanced testing platform based on a digital grid twin protection test bed using a synchronized time source system. The platform evaluates the communication system using radio antennas or optical fiber links by integrating protective relays that operate breakers within the digital twin and record relay events and communication signals. In the experiments, transmitter and receiver relays were configured with inverse time overcurrent and breaker trip detection logic to assess the total time of the communication protection schemes based on the sum of the relay protection element operating time, radio latency, propagation delay, baud rate delay, and relay processing time. These delays were derived from recorded relay events and communication signals from the interface of a real-time simulator set as a digital grid twin. The test bed successfully simulated various electrical faults along a distribution line while ensuring effective and reliable point-to-point communication between transmitter and receiver relays. The radio antenna communication system exhibited latency because of the radio. This latency depends on the baud rate setting and type of radio application; in general, the higher the baud rate, the lower the radio latency. The measured radio latency (for Mirrored Bits with an encryption card at 9,600 bps) was about 9–10 ms. Additionally, calculated propagation delay per mile for radio antennas and optical fiber was 5.36 µs/mi and 8.04 µs/mi, respectively. Optical fiber communication did not demonstrate radio latency. Instead, the protection element operating time depends mainly on the protection logic function set in the relay, and the relay processing time depends on the processing rate of the relay in samples per power system cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. Furthermore, the results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Hardware design and testing of the generic rear transition module for the global trigger subsystem of ATLAS Phase-II Upgrade

Abstract In the framework of the ATLAS experiment’s Phase-II Upgrade at the High-Luminosity Large Hadron Collider (HL-LHC), new and improved trigger hardware and algorithms will be implemented onto a single-level, 10 μs-latency architecture. The Global Trigger is a new subsystem which will bring event-filter capabilities by performing offline-like algorithms on full-granularity calorimeter data. The implementation of the functionality is firmware-focused and composed of several processing nodes, which are hosted on identical hardware, made up of an Advanced Telecommunications Computing Architecture (ATCA) front board, called Global Common Module (GCM), and a rear transition module (RTM), called Generic RTM (GRM). The GRM, which was developed to mitigate the risks deriving from the complex design and power management of the GCM, features an advanced Xilinx Versal Prime system-on-chip and can handle communication with the GCM and Front-End Link eXchange (FELIX) subsystem and trigger processors through 124 25.8 Gb/s transceiver links, for readout and control. Additionally, the GRM mounts a Low-Power GigaBit Transceiver (lpGBT) chip which enables emulation of the detector front-ends for integration tests. This paper presents the GRM hardware design and its testing.

Instruments & Instrumentation↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Enhanced Machine-Learning Flow for Microwave-Sensing Systems for Contaminant Detection in Food

The presence of foreign bodies in packaged food is a serious concern for both fnal consumers (allergies, injuries, choking) and food manufacturers (reputation and economic losses). In particular, low-density plastics, glass and wood splinters are hard to detect even by the most advanced X-ray imagers. One solution is Machine-Learning-based Microwave Sensing (MLMWS): a non-invasive, contactless, and real-time method which uses a machine-learning (ML) classifer to analyze the scattered microwaves from the irradiated target object. In this paper, we want to extend our previous work about contaminant detection in cocoa-hazelnut spread jars by proposing an enhanced ML flow to increase the accuracy of the ML classifier. For the first time in this case study, we use a multi-class classifier, we train it with scattering parameters measured at multiple microwave frequencies, with a new pre-processing scaler, data augmentation, quantization-aware training and a pruning schedule. The results show a contaminant detection multi-class accuracy of 94.167% with a latency of 26 µs when targeting an AMD/Xilinx Kria K26 FPGA. Finally, we released our datasets publicly to OpenML.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Commercial building loads providing ancillary services In PJM

The adoption of low carbon energy technologies such as variable renewable energy and electric vehicles, coupled with the efficacy of energy efficiency to reduce traditional base load has increased the uncertainty inherent in the net load shape. Handling this variability with slower, traditional resources leads to inefficient system dispatch, and in some cases may compromise reliability. Grid operators are looking to future energy technologies, such as automated demand response (DR), to provide capacity-based reliability services as the need for these services increase. While DR resources are expected to have the flexibility characteristics operators are looking for, demonstrations are necessary to build confidence in their capabilities. Additionally, building owners are uncertain of the monetary value and operational burden of providing these services. To address this, the present study demonstrates the ability of demand response resources providing two ancillary services in the PJM territory, synchronous reserve and regulation, using an OpenADR 2.0b signaling architecture. The loads under control include HVAC and lighting at a big box retail store and variable frequency fan loads. The study examines performance characteristics of the resource: the speed of response, communications latencies in the architecture, and accuracy of response. It also examines the frequency and duration of events and the value in the marketplace which can be used to examine if the opportunity is sufficient to entice building owners to participate.

MacDonald, Jason↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

FPGA Acceleration of GCN in Light of the Symmetry of Graph Adjacency Matrix

Graph Convolutional Neural Networks (GCNs) are widely used to process large-scale graph data. Different from deep neural networks (DNNs), GCNs are sparse, irregular, and unstructured, posing unique challenges to hardware acceleration with regular processing elements (PEs). In particular, the adjacency matrix of a GCN is extremely sparse, leading to frequent but irregular memory access, low spatial/temporal data locality and poor data reuse. Furthermore, a realistic graph usually consists of unstructured data (e.g., unbalanced distributions), creating significantly different processing times and imbalanced workload for each node in GCN acceleration. To overcome these challenges, we propose an end-to-end hardware-software co-design to accelerate GCNs on resource-constrained FPGAs with the features including: (1) A custom dataflow that leverages symmetry along the diagonal of the adjacency matrix to accelerate feature aggregation for undirected graphs. We utilize either the upper or the lower triangular matrix of the adjacency matrix to perform aggregation in GCN to improve data reuse. (2) Unified compute cores for both aggregation and transform phases, with full support to the symmetry-based dataflow. These cores can be dynamically reconfigured to the systolic mode for transformation or as individual accumulators for aggregation in GCN processing. (3) Preprocessing of the graph in software to rearrange the edges and features to match the custom dataflow. This step improves the regularity in memory access and data reuse in the aggregation phase. Moreover, we quantize the GCN precision from FP32 to INT8 to reduce the memory footprint without losing the inference accuracy. We implement our accelerator design in Intel Stratix10 MX FPGA board with HBM2, and demonstrate 1.3x-110.5x improvement in end-to-end GCN latency as compared to the state-of the-art FPGA implementations, on the graph datasets of Cora, Pubmed, Citeseer and Reddit.

Nair, Gopikrishnan R.↗