Results for “signalling” — Page 33

DOE OSTI2015

Ground-referenced single-ended signaling connected graphics processing unit multi-chip module

A system of interconnected chips comprising a multi-chip module (MCM) includes a first processor chip, a graphics processing cluster (GPC) chip, and an MCM package configured to include the first processor chip, the GPC chip, and an interconnect circuit. The first processor chip is configured to include a first ground-referenced single-ended signaling interface circuit. A first set of electrical traces fabricated within the MCM package and configured to couple the first single-ended signaling interface circuit to the interconnect circuit. The GPC chip is configured to include a second single-ended signaling interface circuit and to execute shader programs. A second set of electrical traces fabricated within the MCM package and configured to couple the second single-ended signaling interface circuit to the interconnect circuit. In one embodiment, each single-ended signaling interface advantageously implements ground-referenced single-ended signaling.

Dally, William J.↗

DOE OSTI2020

Communication device, spread-spectrum receiver, and related method using normalized matched filter for improving signal-to-noise ratio in harsh environments

A communication device, a method of operating a communication device, and a spread-spectrum receiver are disclosed. The method includes receiving an incoming RF signal, demodulating the incoming RF signal to generate a baseband signal, filtering the baseband signal with a normalized matched filter having filter characteristics matched to a pulse-shaping filter of the transmitter that generated the incoming RF signal, and extracting a received signal from a normalized output generated by the normalized matched filter. As a result, interferences and noise from harsh environments may be suppressed.

42 ENGINEERING↗

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↗

DOE OSTI2023

Vehicle trajectory prediction near or at traffic signal

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d x and a longitudinal host vehicle speed v x that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase P t and an traffic light signal timing T t ; obtaining a time of day TOD; providing the host vehicle-traffic light distance d x , the longitudinal host vehicle speed v x , the traffic light signal phase P t , the traffic light signal timing T t , and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Note: On-line weak signal detection via adaptive stochastic resonance

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Parameter-induced stochastic resonance based on spectral entropy and its application to weak signal detection

The parameter-induced stochastic resonance based on spectral entropy (PSRSE) method is introduced for the detection of a very weak signal in the presence of strong noise. The effect of stochastic resonance on the detection is optimized using parameters obtained in spectral entropy analysis. Upon processing employing the PSRSE method, the amplitude of the weak signal is enhanced and the noise power is reduced, so that the frequency of the signal can be estimated with greater precision through spectral analysis. While the improvement in the signal-to-noise ratio is similar to that obtained using the Duffing oscillator algorithm, the computational cost reduces from O(N{sup 2}) to O(N). The PSRSE approach is applied to the frequency measurement of a weak signal made by a vortex flow meter. The results are compared with those obtained applying the Duffing oscillator algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Burst design and signal processing for the speed of sound measurement of fluids with the pulse-echo technique

The pulse-echo technique determines the propagation time of acoustic wave bursts in a fluid over a known propagation distance. It is limited by the signal quality of the received echoes of the acoustic wave bursts, which degrades with decreasing density of the fluid due to acoustic impedance and attenuation effects. Signal sampling is significantly improved in this work by burst design and signal processing such that a wider range of thermodynamic states can be investigated. Applying a Fourier transformation based digital filter on acoustic wave signals increases their signal-to-noise ratio and enhances their time and amplitude resolutions, improving the overall measurement accuracy. In addition, burst design leads to technical advantages for determining the propagation time due to the associated conditioning of the echo. It is shown that the according operation procedure enlarges the measuring range of the pulse-echo technique for supercritical argon and nitrogen at 300 K down to 5 MPa, where it was limited to around 20 MPa before.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗

NASA NTRS1982

Propagation of wide bandwidth signals through strongly turbulent ionized media

Analytic and numerical techniques are presented which directly address the problem of propagation of wide bandwidth signals through random ionized media. This work is applicable to the problems of satellite communication and space based radar observation through a disturbed ionospheric propagation channel that would result from a high altitude chemical release or nuclear detonation. An analytic solution is presented for the two-position, two-frequency mutual coherence function for spherical wave propagation in the strong scattering limit. This solution is used to derive simple expressions for the power impulse response function and to determine its relationship to the important parameters of decorrelation distance and coherence bandwidth which describe the disturbed propagation channel. Results for mean time delay and time delay jitter are presented and compared to direct simulation results and to other theoretical calculations. A numerical/analytical solution to the parabolic wave equation is presented in the form of a multiple phase-screen (MPS) propagation simulation. In this very general solution technique, the random medium is divided into a finite number of layers. The field fluctuation through each layer is obtained by replacing the layer by a centrally located thin phase-screen, whose statistical properties are determined from the statistics of the electron-density irregularities. The waveform then propagates from phase-screen to phase-screen via an exact solution to the Fresnel-Kirchnoff equation. For wide bandwidth waveforms, numerical solutions are obtained at a number of discrete frequencies centered about the carrier and then time-domain solutions are obtained by Fourier transform techniques. Detailed results are presented for a number of interesting cases including propagation of a 20 MHz bandwidth signal through a finite barium cloud at a carrier frequency of 100 MHz. One of the major uses of the MPS propagation simulation has been to provide realizations of the received signal after propagation through a disturbed channel. The MPS simulation obtains a general solution of the parabolic wave equation under both weak and strong scattering conditions. A second technique to directly obtain realizations of wide bandwidth waveforms is presented here. This technique is limited to the case of strong scattering but requires only a fraction of the computer resources needed for MPS signal generation. Detailed comparisons of the two signal generation techniques are presented.

Random Media↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗

DOE OSTI2025

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals (Presentation)

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Erbium gain effects on Stokes signal performance in a Fabry–Perot Brillouin Erbium fiber laser

The effect of the Erbium gain on the Stokes signal performance is experimentally presented in this paper. Linear Brilllouin Erbium fiber laser cavity is tested under different 1480 nm pump power ranges (0–125 mW). Different Brillouin pump (Bp) power values of 2, 4 and 6 dBm are used in this study. Stokes signal net gain and threshold power are recorded at all generated Stokes signals for these Bp powers values. 2 dBm Bp power shows higher net gain, low threshold, large Stokes numbers and low required 1480 nm pump power compared to 4 and 6 dBm. Up to 8.3 dB net gain, threshold of − 10.3 dBm, 24 Stokes signals and with a maximum 1480 nm pump power of 92.2 mW are recorded for 2 dBm Bp power. Stokes signal threshold is recorded at the near end of the single mode fiber at which Nth order Stokes is generated.

42 ENGINEERING↗

DOE OSTI2014

Kirenol inhibits adipogenesis through activation of the Wnt/β-catenin signaling pathway in 3T3-L1 adipocytes

Highlights: • Kirenol inhibits the adipogenic transcription factors and lipogenic enzymes. • Kirenol stimulates the Wnt/β-catenin signaling pathway components. • Kirenol inhibits adipogenesis through activation of the Wnt/β-catenin signaling pathway. - Abstract: Kirenol, a natural diterpenoid compound, has been reported to possess anti-oxidant, anti-inflammatory, anti-allergic, and anti-arthritic activities; however, its anti-adipogenic effect remains to be studied. The present study evaluated the effect of kirenol on anti-adipogenesis through the activation of the Wnt/β-catenin signaling pathway. Kirenol prevented intracellular lipid accumulation by down-regulating key adipogenesis transcription factors [peroxisome proliferator-activated receptor γ (PPARγ), CCAAT/enhancer binding proteins α (C/EBPα), and sterol regulatory element binding protein-1c (SREBP-1c)] and lipid biosynthesis-related enzymes [fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC)], as well as adipocytokines (adiponectin and leptin). Kirenol effectively activated the Wnt/β-catenin signaling pathway, in which kirenol up-regulated the expression of low density lipoprotein receptor related protein 6 (LRP6), disheveled 2 (DVL2), β-catenin, and cyclin D1 (CCND1), while it inactivated glycogen synthase kinase 3β (GSK3β) by increasing its phosphorylation. Kirenol down-regulated the expression levels of PPARγ and C/EBPα, which were up-regulated by siRNA knockdown of β-catenin. Overall, kirenol is capable of inhibiting the differentiation and lipogenesis of 3T3-L1 adipocytes through the activation of the Wnt/β-catenin signaling pathway, suggesting its potential as natural anti-obesity agent.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Activation of AMPA receptor promotes TNF-α release via the ROS-cSrc-NFκB signaling cascade in RAW264.7 macrophages

The relationship between glutamate signaling and inflammation has not been well defined. This study aimed to investigate the role of AMPA receptor (AMPAR) in the expression and release of tumor necrosis factor-alpha (TNF-α) from macrophages and the underlying mechanisms. A series of approaches, including confocal microscopy, immunofluorescency, flow cytometry, ELISA and Western blotting, were used to estimate the expression of AMPAR and downstream signaling molecules, TNF-α release and reactive oxygen species (ROS) generation in the macrophage-like RAW264.7 cells. The results demonstrated that AMPAR was expressed in RAW264.7 cells. AMPA significantly enhanced TNF-α release from RAW264.7 cells, and this effect was abolished by CNQX (AMPAR antagonist). AMPA also induced elevation of ROS production, phosphorylation of c-Src and activation of nuclear factor (NF)-κB in RAW264.7 cells. Blocking c-Src by PP2, scavenging ROS by glutathione (GSH) or inhibiting NF-κB activation by pyrrolidine dithiocarbamate (PDTC) decreased TNF-α production from RAW264.7 cells. We concluded that AMPA promotes TNF-α release in RAW264.7 macrophages likely through the following signaling cascade: AMPAR activation → ROS generation → c-Src phosphorylation → NF-κB activation → TNF-α elevation. The study suggests that AMPAR may participate in macrophage activation and inflammation. - Highlights: • AMPAR is expressed in RAW264.7 macrophages and is upregulated by AMPA stimulation. • Activation of AMPAR stimulates TNF-α release in macrophages through the ROS-cSrc-NFκB signaling cascade. • Macrophage AMPAR signaling may play an important role in inflammation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Indispensable role of Notch ligand-dependent signaling in the proliferation and stem cell niche maintenance of APC-deficient intestinal tumors

Ligand-dependent activation of Notch signaling is required to maintain the stem-cell niche of normal intestinal epithelium. However, the precise role of Notch signaling in the maintenance of the intestinal tumor stem cell niche and the importance of the RBPJ-independent non-canonical pathway in intestinal tumors remains unknown. Here we show that Notch signaling was activated in LGR5{sup +ve} cells of APC-deficient mice intestinal tumors. Accordingly, Notch ligands, including Jag1, Dll1, and Dll4, were expressed in these tumors. In vitro studies using tumor-derived organoids confirmed the intrinsic Notch activity-dependent growth of tumor cells. Surprisingly, the targeted deletion of Jag1 but not RBPJ in LGR5{sup +ve} tumor-initiating cells resulted in the silencing of Hes1 expression, disruption of the tumor stem cell niche, and dramatic reduction in the proliferation activity of APC-deficient intestinal tumors in vivo. Thus, our results highlight the importance of ligand-dependent non-canonical Notch signaling in the proliferation and maintenance of the tumor stem cell niche in APC-deficient intestinal adenomas. - Highlights: • Notch signaling is activated in LGR5{sup +ve} cells of APC-deficient intestinal tumors. • Lack of Jag1 but not RBPJ disrupts stem cell niche formation in those tumors. • Lack of Jag1 reduces the proliferation activity of APC-deficient intestinal tumors.

60 APPLIED LIFE SCIENCES↗