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At least 469 records · Page 26

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DOE OSTI2021

Offline Arterial Signal Timing Optimization based on Virtual Phase Link Model - A Real-world Case Study

Conventional signal timing for arterial usually takes bottom-up approaches. Engineers optimize each individual intersection first and then coordinate them by adjusting the offsets. This work is based on the Virtual Phase-Link (VPL) model, a street traffic model designed for online traffic model predictive control, to obtain a top-down offline arterial signal timing. We Studied the Shallowford Rd. in Chattanooga, TN and found that the inconsistency in intersection capacities along the arterial could lead to some intersections becoming bottlenecks. Signal timing is a significant factor that affect the intersection capacities. We realized that the VPL-based model can guarantee the consistency in intersections along an arterial. We therefore adopted the VPL-based model and developed an offline signal timing optimization approach. The proposed timing derived from the VPL-based offline signal timing optimization showed very good results in simulation. The Chattanooga Department of Transportation adopted the optimized timing obtained from the proposed approach and gave positive feedbacks to the research team. We also collected field experiment data, which demonstrated overall energy reductions and speed improvements on some sections of the Shallowford Rd. arterial. We will continue the experiment when the COVID-19 pandemic impact subsides to have a more robust quantitative evaluation.

Wang, Qichao↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

This report presents the research, design, and implementation of a Toroid Signal Processing Container with Redis Integration, conducted during my internship assignment. The project focused on developing a modular signal processing sys- tem capable of performing baseline correction, droop compensation, and real- time data communication for beam diagnostic signals. Using Redis as a message broker and configuration manager, the system ensures modularity, scalability, and efficient inter-process communication. Signal correction algorithms, such as Asymmetric Least Squares (ALS) baseline smoothing and a numerical droop correction approach, were implemented and tested using Linac beam data. The results confirm that this system improves signal fidelity and supports real-time analysis requirements, offering valuable contributions to the field of beam in- strumentation and data acquisition systems.

Bowers, Elliot [Cabrillo Coll.; Fermilab]↗

DOE OSTI2025

Wiener Filter Deconvolution for Analog Signals

This project explores the use of Wiener deconvolution to recover an original signal that has been distorted by a known transfer function and by noise. A simulated Gaussian pulse was used as the test signal, and a transfer function was applied in the frequency domain to model the system distortion. Controlled noise was then introduced to approximate real-world signal degradation. A Wiener filter was implemented to reverse the effects of the transfer function while minimizing the influence of noise. The recovered signal was compared with the original pulse to evaluate the effectiveness of the filter. The results demonstrate that Wiener deconvolution offers a stable and effective approach to signal recovery, balancing complete transfer function inversion and noise suppression.

Campbell, Savanna [Fermilab]↗

DOE OSTI2022

A synthetic cell density signal can drive proliferation in chick embryonic tendon cells and tendon cells from a full size rooster can produce high levels of procollagen in cell culture

Cell density signaling drives tendon morphogenesis by regulating both procollagen production and cell proliferation. The signal is composed of a small, highly conserved protein (SNZR P) tightly bound to a tissue-specific, unique lipid (SNZR L). This allows the complex (SNZR PL) to bind to the membrane of the cell and locally diffuse over a radius of ~1 mm. The cell produces low levels of this signal but the binding to the membrane increases with the number of tendon cells in the local environment. In this article SNZR P was produced in E.coli and SNZR L was chemically synthesized. The two bind together when heated to 60 °C in the presence of Ca ++ and Mg ++ and the synthesized SNZR PL at ng/ml levels can replace serum. Adding SNZR PL to the medium was also tested on primary tendon cells from adult roosters. The older cells were in a maintenance state in vivo and in cell culture they proliferate more slowly than embryonic cells. Nevertheless, after reaching a moderately high cell density, they produced high levels of procollagen similar to the embryonic cells. This data was not expected from older cells but suggests that adult tendon cells can regenerate the tissue after injury when given the correct signals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Effect of a Fine-Scale Layered Structure of the Atmosphere on Infrasound Signals from Fragmenting Meteoroids

We investigate the influence of a fine-scale (FS) layered structure in the atmosphere on the propagation of infrasound signals generated by fragmenting meteoroids. Using a pseudo-differential parabolic equation (PPE) approach, we model broadband acoustic signals from point sources at altitudes of 35–100 km. The presence of FS fluctuations in the stratosphere (37–45 km) and the lower thermosphere (100–120 km) modifies ray trajectories, causing multiple arrivals and prolonged signal durations at ground stations. In particular, meteoroids fragmenting at 80–100 km can produce two distinct thermospheric arrivals beyond 150 km range, while meteoroids descending to 50 km or below yield weak, long-lived arrivals within the acoustic shadow zone via antiguiding propagation and diffraction. Comparison with observed infrasound data confirms that FS-layered inhomogeneities can account for multi-arrival “N-waves,” broadening potential interpretations of meteoroid signals. The results also apply to other atmospheric-entry objects, such as sample return capsules, emphasizing how FS structure impacts shock wave propagation. In conclusion, our findings advance understanding of wavefield evolution in a layered atmosphere and have broad relevance for global infrasound monitoring of diverse phenomena (e.g., re-entry capsules, rocket launches, and large-scale explosions).

Aeroacoustics↗

DOE OSTI2025

Nanoparticle-mediated antagonism of sustained endosomal signaling of the calcitonin receptor-like receptor provides enhanced and persistent relief of oral cancer pain

By improving the delivery and tumor retention of chemotherapeutics, nanomedicines hold potential for cancer treatment. The usefulness of nanoparticle (NP)-encapsulated analgesics for the cancer pain treatment is comparatively unexplored. We investigated whether NPs encapsulating olcegepant (OCP), an antagonist of the calcitonin receptor-like receptor (CLR) for the calcitonin gene-related peptide (CGRP), effectively relieved oral cancer pain in mice. Because persistent endosomal CLR signaling in Schwann cells mediates craniofacial pain, we reasoned that the predisposition of NPs to accumulate in endosomes could be leveraged to effectively relieve oral cancer pain. By expressing biosensors for activated CLR, Gα proteins and β-arrestins in HEK293T and Schwann cells, we found that CGRP activates CLR signaling first at the plasma membrane and then in early, late and recycling endosomes and the cis- and trans-Golgi apparatus. We synthesized biocompatible NPs encapsulating OCP and fluorophores by integrating hydrophobic ion pairing nanoformulation with Flash NanoPrecipitation. NPs slowly released OCP and accumulated in early endosomes, leading to sustained inhibition of endosomal CLR signaling in HEK293T and Schwann cells. Oral cancers were established in mice, which led to heightened pain-like responses. After intra-tumoral injection, NPs were retained in tumors for at least one week. OCP-loaded NPs almost completely reversed allodynia and hyperalgesia for a prolonged period, whereas unencapsulated OCP had small and transient effects. The NP accumulation in endosomal sites of pain signaling, the sustained release of antagonist, and the retention of NPs in tumors explain their beneficial actions. Thus, NP-encapsulation holds promise for the relief of painful cancers that are inadequately treated by opioids.

Calcitonin gene-related peptide↗

DOE OSTI2020

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. Here, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept

Here, we investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Evaluation of a new DIII-D Doppler backscattering system for higher wavenumber measurement and signal enhancement

The high density fluctuation poloidal wavenumber, k θ (k θ > 8 cm –1 , k θ ρ s > 5, ρs is the ion gyro radius using the ion sound velocity), measurement capability of a new Doppler backscattering (DBS) system at the DIII-D tokamak has been experimentally evaluated. In DBS, wavenumber (k) matching becomes more important at higher wavenumbers, owing to the exponential dependence of the measured signal loss factor on wave vector mismatch. Wave vector matching allows for the Bragg scattering condition to be satisfied, which minimizes the signal loss at higher k’s. In the previous DBS system, without toroidal wave vector matching, the measured DBS signal-to-noise ratio at higher k θ (>8 cm –1 ) is substantially reduced, making it difficult to measure higher k θ turbulence. The new DBS system has been optimized to access higher wavenumber, k θ ≤ 20 cm –1 , density turbulence measurement. The optimization hardware addresses fluctuation wave vector matching using toroidal steering of the launch mirror to produce a backscattered signal with improved intensity. The probe’s sensitivity to high-k density fluctuations has been increased by approximately an order of magnitude compared to the old system that has been in use at DIII-D. Note that typical measurement locations are above or below the tokamak midplane on the low field side with normalized radial ranges of 0.5–1.0. As a result, the new DBS probe system with the toroidal matching of fluctuation wave vectors is thought to be critical to understanding high-k turbulent transport in fusion-relevant research at DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Fatty acid-mediated signaling as a target for developing type 1 diabetes therapies

Type 1 diabetes (T1D) is an autoimmune disease in which pro-inflammatory and cytotoxic signaling drive the death of the insulin-producing ß cells. This complex signaling is regulated in part by fatty acids and their bioproducts, making them excellent therapeutic targets. We provide an overview of the fatty acid actions on ß cells by discussing how they can cause lipotoxicity or regulate inflammatory response during insulitis. Additionally, we also discuss how diet can affect the availability of fatty acids and disease development. Finally, we discuss development avenues that need further exploration. Fatty acids, such as hydroxyl fatty acids, ω-3 fatty acids and their downstream products, are druggable candidates that promote protective signaling. Inhibitors and antagonists of enzymes and receptors of arachidonic acid and free fatty acid metabolites, which cause pro-inflammatory and cytotoxic responses, have the potential to be developed as therapeutic targets also. Further, because diet is the main source of fatty acid intake in humans, balancing protective and pro-inflammatory/cytotoxic fatty acid levels through dietary therapy may have beneficial effects, delaying T1D progression. Therefore, therapeutic interventions targeting fatty acid signaling hold potential as avenues to treat T1D.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Adaptive Online Multivariate Signal Extraction With Locally Weighted Robust Polynomial Regression

High-frequency, multivariate data collected in real-time and used to control or make decisions regarding a process’ operation often contain some noise and outliers. Thus, a method to extract the signal is needed in order to reduce the number and magnitude of control-based adjustments that are implemented. Such a method must be (i) online, depending only on past and current observations; (ii) fast, producing a smooth value more quickly than the measurement frequency; (iii) robust, ignoring brief bursts of erroneously measured values; (iv) multivariate, ignoring observations that are jointly unusual; (v) adaptive, adjusting to periods of rapid fluctuation in the signal versus periods of stability; and (vi) purely data-driven, not incorporating any information about the process from which the data are collected. Most existing methods are only able to address a subset of these six features. Furthermore, we also require the method to be nonlinear, providing a local nonlinear estimate of the signal. In this work, we propose a novel, real-time signal extraction method based on a local, robust polynomial fit. We demonstrate the performance of our method compared to a state-of-the-art competitor through simulation. For illustration, the methodology is applied to data collected from a reverse osmosis water treatment process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Signal processing to reduce dark noise impact in precision timing

We introduce a technique to mitigate the effects of low frequency noise on precision timing. The example of Dark Count Noise Rate (DCR) in Silicon Photomultipliers (SiPMs) is emphasized. This technique exploits the correlation between time shifts on the leading edge of a signal and the residual slope of the baseline (due to noise) which remains after baseline subtraction. In fast timing applications (such as for Time-of-flight particle ID) the signal arrival time is typically captured on the signal leading edge. The signal risetime is often fixed by the physics of the sensor and input circuit. Then accurate pulse timing can be achieved by correcting a leading edge threshold time (depending on a slope proportional to both the Amplitude and the risetime) to a “constant fraction” time. This compensation for time walk due to amplitude fluctuations breaks down once we introduce interference from low frequency noise on the leading edge. In this paper we demonstrate that an additional measurement of the slope at threshold can be used to correct for this noise jitter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2026

Small-Signal Analysis of Current-Limiting Grid-Forming Inverters

Grid-forming (GFM) inverters are equipped with a current limiter to protect the device during grid disturbances. Because the intervention of the current limiter can compromise the transient stability of the GFM inverter, various types of current limiter designs and additional frequency-stabilization control methods have been proposed in recent literature to aid in transient GFM inverter stability; however, the small-signal stability implications of adding these additional control blocks during, in particular, off-nominal conditions are not fully understood. To address this challenge, this paper presents a generic small-signal model of a GFM inverter that incorporates various types of current limiters and frequency stability-enhancing controls. With the proposed model, we analyze the root causes of inverter instability, which reveals two critical design trade-offs. First, due to current-limiter engagement caused by a disturbance, the GFM inverter dynamics are altered, which can lead to voltage oscillations. Second, additional frequency-stabilization controls can improve the small-signal stability of the GFM inverter, but at the cost of reduced power and voltage support to the grid. Comprehensive hardware experiments validate the theoretical concept and analysis. The findings in this work underscore the importance of incorporating both transient-response and small-signal dynamics requirements into the design procedure of GFM current limiters and frequency-stabilization controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

We report a real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.

42 ENGINEERING↗

DOE OSTI2025

Unified Model of Current-Limiting Grid-Forming Inverters for Large-Signal Analysis

Grid-forming (GFM) inverters can hardly withstand any overloading. As such, GFM inverters need a current limiter in their control system to avoid hardware damage during overloading or grid disturbances. When the current limiter is engaged for inverter protection, however, it dynamically changes the inverter output impedance, which can significantly affect the system behavior against large disturbances. For example, when the current limiter curtails the output power, the primary controller of the GFM inverter-which generates the voltage and angle reference-is prone to losing synchronization with the grid. This can cause large-signal instability in the network. This paper presents a unified GFM current-limiter model to gain a deeper understanding of the impact of the GFM inverter current limiting on large-signal instability and other system behaviors. The unified model captures several well-known current limiters for GFM inverters, and it reveals their large-signal equivalence characterized by the limiter angle. We use the unified model to present in-depth insights into the driving forces that cause large-signal instability in GFM inverter-driven grids. Further, this paper illustrates that the unified model can be used as a tool for assessing the system-level impacts of various GFM current-limiter designs during grid disturbances. Using numerical, analytical, electromagnetic transient models, and a hardware setup, the validity of the unified model is evaluated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Harmonic Signals Dataset

The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗