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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sensing Technology Survey for Obstacle Detection in Vegetation

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

MSU IETC ML for Modbus (AN EDGE)

This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.

Communication Protocol↗
DOE OSTI2024

Solid, structured composite neutron detectors with high dynamic range capability

Neutron detectors are essential across disciplines such as fundamental science, nuclear security, safeguards, and civilian applications. While 3 He-filled gas proportional counters have long been revered for their efficacy in detecting thermal neutrons and praised for their efficiency, neutron/gamma discrimination, and stability, the scarcity of 3He has spurred a search for alternatives. Here, we explore a solid structured scintillating particle composite (SPC) consisting of 6 Li-containing scintillating glass particles within an acrylic matrix as a neutron detector for high dynamic range applications. We show for the first time that an SPC neutron detector can boast an intrinsic detection efficiency of 0.261% for pure 252 Cf fission neutrons and an overall neutron detection efficiency of (0.546 ± 0.003)% at the Neutron Free-in-Air facility while being able to function in an intense gamma-ray environment. We also show that the SPC neutron detector supports fast neutron capture times and enables a dual-readout scheme that extends the detector dynamic range to high incident neutron fluxes. A scalable fabrication process allows for tailoring the SPC detector properties to the requirements of specific applications. Good agreement is found between the experimental results taken with a National Institute of Standards and Technology traceable 252 Cf source and the coupled MCNP6 and optical-ray-tracing simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Improving Fission Products at CARIBU: Near Field Detection (Q3/FY20 Quarterly Progress Report)

Following the report from last quarter where we reported the experimental procedures, data acquisition and filtering process for the beta decay of 156 Eu, in the past quarter we have worked on establishing the efficiency for detecting the radiation emitted from this isotope with simulations of the HPGe detector and we have started investigating the beta detector energy deposition spectra.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Chelation ion chromatography as an automated, and cost-effective analytical technique for REE determination: method development and applications

Rare earth elements (REEs), as critical minerals, have important uses in modern energy and technologies, yet are vulnerable to potential supply chain disruptions. To establish domestic REE supply chain, efficient REE detection methods for resource characterization and mineral processing will be needed to accelerate innovations for domestic REE recovery. This study developed a rapid, novel, and cost-effective for REE detection method using ion chromatography (IC) for aqueous samples. Various REE-targeted eluent gradients and post-column agent compositions were tested on the chelation ion chromatography (CIC) with UV-vis detector for optimal separation and quantification of REEs within approximately 20 min. The single-channel pump to deliver the post-column solution to UV-vis detector was replaced with a 4-channel gradient pump, to increase operation and maintenance efficiencies. After method optimization, resulting calibration curves for more than ten REEs achieved high coefficients of determination (R2>0.999) and low relatively standard deviations (below 3.24%), demonstrating sub-ppm level detection limits (0.0897 to 0.1149 mg/L). The reliability of the CIC method was validated through comparison with inductively coupled plasma mass spectrometry (ICP-MS), showing strong agreement in REE recovery from certified standards. The impact of metal ions and salts on REE recovery using CIC was also systematically investigated. CIC consistently exhibited reliable performance in the presence of salt solutions such as NaCl and Na₂SO₄ (up to 10,000 mg/L). Our study also found the presence of high concentrations of Al ions (at 10,000 mg/L) significantly influenced REE determination, and elevated concentrations of Ca ions affected the recovery of specific REEs, including La, Ce, and Pr. The CIC method was further tested on REE-containing eluents from solvent extraction tests out of fly ash leachates. REE detection from these real processing fluids were reported to achieve 90% to 100% recovery rate from our IC method, compared to ICP-MS results. This study underscores the potential of CIC as a reliable and efficient alternative for REE determination in complex matrices. It also highlights the importance of minimizing select interfering metal ions in solutions to ensure accurate results. The REE CIC method presents a promising, low-maintenance, salt-tolerant, and cost-effective alternative to traditional analytical methods for REE analysis.

detection of rare earth elements (REE)↗
DOE OSTI2020

Multi-messengers at ultra-high energies with the Pierre Auger Observatory

The Pierre Auger Observatory is an Ultra-High Energy Cosmic Ray (UHECR) detector which has studied cosmic particles with energies above and around 10 18 eV for more than 15 years. It has proved to be the most competitive instrument at these energies and has produced a wealth of valuable results, improving our understanding of UHECRs. A complete understanding of these highest energy particles is crucial to understand the extreme astrophysical events in which they are produced and accelerated, as well as their propagation to Earth. In the same range of energies, UHE photons and neutrinos are of paramount importance as, being electrically neutral, they point back to their origin while charged particles are deflected in the galactic and extragalactic magnetic fields. The flux of extragalactic photons, neutrinos, and cosmic rays are believed to be highly linked, by their origin and their interactions. Each messenger provides different information about the potential sources, and having detection means for all four messengers, including gravitational waves, allows us to shed light on energetic sources of astroparticles. The Pierre Auger Observatory benefits from a large exposure and a good angular resolution, and is efficient in detecting UHE photons and neutrinos. These performances make possible follow-up searches for events detected by gravitational waves, such as the binary mergers observed by the LIGO/Virgo detectors, or any other energetic sources of particles.

Binary black hole mergers↗
DOE OSTI2024

Real-time signal detection for Cyclotron Radiation Emission Spectroscopy measurements using antenna arrays

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40 meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. Here, in this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Performances of a new generation tracking detector: the MEG II cylindrical drift chamber

Abstract The cylindrical drift chamber is the most innovative part of the MEG II detector, the upgraded version of the MEG experiment. The MEG II chamber differs from the MEG one because it is a single volume cylindrical structure, instead of a segmented one, chosen to improve its resolutions and efficiency in detecting low energy positrons from muon decays at rest. In this paper, we show the characteristics and performances of this fundamental part of the MEG II apparatus and we discuss the impact of its higher resolution and efficiency on the sensitivity of the MEG II experiment. Because of its innovative structure and high quality resolution and efficiency the MEG II cylindrical drift chamber will be a cornerstone in the development of an ideal tracking detector for future positron-electron collider machines.

Physics↗
DOE OSTI2025

Commission < 5 µm spatial resolution x-ray imager for High Resolution Turbulence experiments

This report presents the successful development, commissioning, and deployment of the Scintillation-Based X-ray Imager (SBXI) for the High-Resolution Turbulence (HRT) campaign at the National Ignition Facility (NIF). The SBXI system was designed to overcome the spatial resolution and efficiency limitations of conventional x-ray imaging detectors. Through careful system engineering, rigorous laboratory characterization, and experimental validation at NIF, the SBXI has demonstrated spatial resolution at the object plane of 5 μm (±0.5 μm) and a detective quantum efficiency (DQE) of 34% at 7.2 keV, both of which surpass the requirements set by the campaign, and meeting the L2 milestone. The system’s deployment enables high-fidelity imaging of fine-scale hydrodynamic instabilities, directly supporting advanced turbulence model validation and future high energy density physics research.

42 ENGINEERING↗
DOE OSTI2025

Next-generation neutron detection using a 6 Li glass scintillator composite

Neutron detectors are crucial in fundamental science, nuclear security, safeguards, and civil applications. 3 He-filled gas proportional counters are the gold standard for thermal neutron detection, prized for their efficiency, neutron/gamma discrimination, and stability; however, the scarcity of 3 He has prompted the search for alternatives. Here, we introduce a neutron detector design based on a scintillating composite consisting of 6 Li glass scintillator particles dispersed in an organic matrix. A detector consisting of this scintillating composite, photomultiplier tubes (PMTs) for optical detection, and electronics for reading out the PMT signal in both pulse and current modes was prototyped and characterized using various neutron and gamma sources. The prototype achieves a measured intrinsic detection efficiency of 6.70 ± 0.01%, a die-away time of 10.3 ± 0.1 μs, a negligible gamma misidentification probability, and response linearity up to at least 3.7 × 10 6 incident neutrons/s established via a cross-calibration technique. This detector holds the potential to outperform traditional 3 He-gas-based neutron detection systems, offering a viable alternative amidst the ongoing 3 He shortage and promising advancements in neutron detection technology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Electron image contrast analysis of mosaicity in rutile nanocrystals using direct electron detection

Direct electron detection provides high detective quantum efficiency, significantly improved point spread function and fast read-out which have revolutionized the field of cryogenic electron microscopy. However, these benefits for high-resolution electron microscopy (HREM) are much less exploited, especially for in situ study where major impacts on crystallographic structural studies could be made. By using direct detection in electron counting mode, rutile nanocrystals have been imaged at high temperature inside an environmental transmission electron microscope. The improvements in image contrast are quantified by comparison with a charge-coupled device (CCD) camera and by image matching with simulations using an automated approach based on template matching. Together, these approaches enable a direct measurement of 3D shape and mosaicity (~1°) of a vacuum-reduced TiO 2 nanocrystal about 50 nm in size. Thus, this work demonstrates the possibility of quantitative HREM image analysis based on direct electron detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A monolithic antineutrino detector for non-intrusive reactor monitoring

Recent advances in organic detection media have found applications in reactor antineutrino physics. One example is the Precision Oscillation and Spectrum Experiment (PROSPECT), which leveraged pulse-shape sensitivity to enable a successful surface deployment at the High Flux Isotope Reactor (HFIR), achieving a signal to background of 4:1. PROSPECT utilized almost 4 tonnes of 6 Li-doped pulse-shape sensitive liquid scintillator in a two-dimensional segmented array. It used a combination of pulse-shape sensitivity and position sensitivity via segmentation to reduce the most prominent form of correlated background for surface detectors — cosmogenic fast neutrons. These new liquids may enable detector designs that bring additional tools for reducing backgrounds while reducing engineering complexity. In this paper, we present an investigation into a detector design that exploits properties of these liquids by maximizing spectral and pulse-shape sensitivity via highly efficient photon detection. The detector utilizes photomultiplier tubes (PMTs) placed at the top and bottom of a right cylinder, with highly reflective white walls. This design sacrifices some position sensitivity for maximal photon efficiency. In conclusion, the design choice has consequences for the identification of the background and antineutrino sensitivity, which we examine.

Pulse shape↗
DOE OSTI2023

Transactive Energy System Deployment Over Insecure Communication Links

Here, in this paper, the privacy and security issues associated with the transactive energy system (TES) deployment over insecure communication links are addressed. In particular, it is ensured that 1) individual agents’ bidding information is kept private throughout hierarchical market-based interactions; and 2) any extraneous data injection attack can be quickly and easily detected. An implementation framework is proposed to enable the cryptography-based enhancement of privacy and security for the deployment of any general hierarchical systems including TESs. Under the proposed framework, a unified cryptography-based approach is developed to achieve both privacy and security simultaneously. Specifically, privacy preservation is realized by an enhanced Paillier encryption scheme, where a block design is proposed to significantly improve computational efficiency. Attack detection is further achieved by an enhanced Paillier digital signature scheme, where a stamp-concatenation mechanism is proposed to enable detection of data replace and reorder attacks. Simulation results verify the effectiveness of the proposed cyber-resilient design for transactive energy systems. Note to Practitioners—This paper is motivated by addressing the issues of cyber resiliency for practically deploying transactive energy system (TES) but it is also applicable to the problem of enhancing the privacy and security for any general hierarchical control systems. TES is an emerging control approach that engages energy suppliers and customers through market operations and uses the price to optimally allocate energy resources. While it has been shown to be promising for power system applications, the underlying market-based interactions raise significant concerns of privacy (data leakage) and security (data tampering). However, existing TES works only focus on the coordination mechanism instead of privacy and security issues. This paper proposes a new cryptography-based TES design for practical deployment. Specifically, to protect privacy, individual supply and demand amounts to be exchanged are all encrypted in a particular way such that the original amounts cannot be inferred from the encrypted amounts, while the desired computation for setting the market clearing price can be carried out over the encrypted amounts, thus generating an encrypted result which, when decrypted, matches that of the same computation over the original amounts. To achieve security, for each exchanged data, its sender generates a particular digital signature which is exchanged together with the data. This enables the receiver to automatically detect the integrity by checking whether a mathematical relationship holds for the pair of data and signature. In our future research, we will investigate more challenging scenarios where some suppliers and customers themselves could be corrupted and purposely submit distorted amounts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2021

Enhancing the sensitivity of the LUX-ZEPLIN (LZ) dark matter experiment to low energy signals

Two-phase xenon detectors, such as that at the core of the forthcoming LZ dark matter experiment, use photomultiplier tubes to sense the primary (S1) and secondary (S2) scintillation signals resulting from particle interactions in their liquid xenon target. This paper describes a simulation study exploring two techniques to lower the energy threshold of LZ to gain sensitivity to low-mass dark matter and astrophysical neutrinos, which will be applicable to other liquid xenon detectors. The energy threshold is determined by the number of detected S1 photons; typically, these must be recorded in three or more photomultiplier channels to avoid dark count coincidences that mimic real signals. To lower this threshold: a) we take advantage of the double photoelectron emission effect, whereby a single vacuum ultraviolet photon has a $\sim20\%$ probability of ejecting two photoelectrons from a photomultiplier tube photocathode; and b) we drop the requirement of an S1 signal altogether, and use only the ionization signal, which can be detected more efficiently. For both techniques we develop signal and background models for the nominal exposure, and explore accompanying systematic effects, including the dependence on the free electron lifetime in the liquid xenon. When incorporating double photoelectron signals, we predict a factor of $\sim 4$ sensitivity improvement to the dark matter-nucleon scattering cross-section at $2.5$ GeV/c$^2$, and a factor of $\sim1.6$ increase in the solar $^8$B neutrino detection rate. Dropping the S1 requirement may allow sensitivity gains of two orders of magnitude in both cases. Finally, we apply these techniques to even lower masses by taking into account the atomic Migdal effect; this could lower the dark matter particle mass threshold to $80$ MeV/c$^2$.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Enhancing the sensitivity of the LUX-ZEPLIN (LZ) dark matter experiment to low energy signals

Two-phase xenon detectors, such as that at the core of the forthcoming LZ dark matter experiment, use photomultiplier tubes to sense the primary (S1) and secondary (S2) scintillation signals resulting from particle interactions in their liquid xenon target. This paper describes a simulation study exploring two techniques to lower the energy threshold of LZ to gain sensitivity to low-mass dark matter and astrophysical neutrinos, which will be applicable to other liquid xenon detectors. The energy threshold is determined by the number of detected S1 photons; typically, these must be recorded in three or more photomultiplier channels to avoid dark count coincidences that mimic real signals. To lower this threshold: a) we take advantage of the double photoelectron emission effect, whereby a single vacuum ultraviolet photon has a $\sim20\%$ probability of ejecting two photoelectrons from a photomultiplier tube photocathode; and b) we drop the requirement of an S1 signal altogether, and use only the ionization signal, which can be detected more efficiently. For both techniques we develop signal and background models for the nominal exposure, and explore accompanying systematic effects, including the dependence on the free electron lifetime in the liquid xenon. When incorporating double photoelectron signals, we predict a factor of $\sim 4$ sensitivity improvement to the dark matter-nucleon scattering cross-section at $2.5$ GeV/c$^2$, and a factor of $\sim1.6$ increase in the solar $^8$B neutrino detection rate. Dropping the S1 requirement may allow sensitivity gains of two orders of magnitude in both cases. Finally, we apply these techniques to even lower masses by taking into account the atomic Migdal effect; this could lower the dark matter particle mass threshold to $80$ MeV/c$^2$.

Akerib, DS↗