Towards increasing the performance of FTICR-MS with signal detection at frequency multiples: Signal theory and numerical study
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Wu, Qinghao (ORCID:000000031665567X)↗
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Results for “signalling”
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.
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Optimizing Transmission of Acoustic Signals to Monitor Internal Conditions of Canisters for Dry Storage of Commercial Spent Nuclear Fuel
Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is stored primarily via two methods regulated by the U.S. Nuclear Regulatory Commission: wet storage in SNF pools and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS is reflected in changes in the internal gas properties; this motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. This paper documents experimental studies conducted on a full-scale canister mock-up to capture the gas-borne signals. Damping materials were pasted on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The results show that the excitation frequency plays an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight. A high signal-to-noise ratio was achieved in the measurements. Next, the acoustic impedance matching layers were introduced, and the gas signal was drastically improved compared with that using no AIM layers.
Spent nuclear fuel (SNF), Canisters, Internal cond↗
Improving Signal Signal-to -Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA
This research presents an innovative approach to improving theSignalSignal-to -Noise Ratio (SNR) in superconducting quantumcomputing readout signals, utilizing adaptive filtering on thecontrol board. Our solution FPGA FPGA-based aims to bolsterquantum computations' reliability by providing a more accurateand precise way to obtain our readout traces and achieve ahigher discernability between excitation states in real real-time.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Signal processing considerations for low signal to noise ratio laser Doppler and phase Doppler signals
The relative performance of current methods used for estimating the phase and the frequency in LDV and phase Doppler applications in low signal to noise ratio conditions is analyzed. These methods include the Fourier analysis and the correlation techniques. Three methods that use the correlation function for frequency and phase estimations are evaluated in terms of accuracy and speed of processing. These methods include: (1) the frequency estimation using zero crossings counting of the auto-correlation function, (2) the Blackman-Tukey method, and (3) the AutoRegressive method (AR). The relative performance of these methods is evaluated and compared with the Fourier analysis method which provides the optimum performance in terms of the Maximum Likelihood (ML) criteria.
Ibrahim, K. M.↗
A genetic screen for dominant chloroplast reactive oxygen species signaling mutants reveals life stage-specific singlet oxygen signaling networks
Plants employ intricate molecular mechanisms to respond to abiotic stresses, which often lead to the accumulation of reactive oxygen species (ROS) within organelles such as chloroplasts. Such ROS can produce stress signals that regulate cellular response mechanisms. One ROS, singlet oxygen ( 1 O 2 ), is predominantly produced in the chloroplast during photosynthesis and can trigger chloroplast degradation, programmed cell death (PCD), and retrograde (organelle-to-nucleus) signaling. However, little is known about the molecular mechanisms involved in these signaling pathways or how many different signaling 1 O 2 pathways may exist. The Arabidopsis thaliana plastid ferrochelatase two (fc2) mutant conditionally accumulates chloroplast 1 O 2 , making fc2 a valuable genetic system for studying chloroplast 1 O 2 -initiated signaling. Here, we have used activation tagging in a new forward genetic screen to identify eight dominant fc2 activation-tagged (fas) mutations that suppress chloroplast 1 O 2 -initiated PCD. While 1 O 2 -triggered PCD is blocked in all fc2 fas mutants in the adult stage, such cellular degradation in the seedling stage is blocked in only two mutants. This differential blocking of PCD suggests that life-stage-specific 1 O 2 -response pathways exist. In addition to PCD, fas mutations generally reduce 1 O 2 -induced retrograde signals. Furthermore, fas mutants have enhanced tolerance to excess light, a natural mechanism to produce chloroplast 1 O 2 . However, general abiotic stress tolerance was only observed in one fc2 fas mutant (fc2 fas2). Together, this suggests that plants can employ general stress tolerance mechanisms to overcome 1 O 2 production but that this screen was mostly specific to 1 O 2 signaling. We also observed that salicylic acid (SA) and jasmonate (JA) stress hormone response marker genes were induced in 1 O 2 -stressed fc2 and generally reduced by fas mutations, suggesting that SA and JA signaling is correlated with active 1 O 2 signaling and PCD. Together, this work highlights the complexity of 1 O 2 signaling by demonstrating that multiple pathways may exist and introduces a suite of new 1 O 2 signaling mutants to investigate the mechanisms controlling chloroplast-initiated degradation, PCD, and retrograde signaling.
59 BASIC BIOLOGICAL SCIENCES↗
Multi-component background learning automates signal detection for spectroscopic data
Abstract Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.
Ament, Sebastian E.↗
Residual Optically Stimulated Luminescent (OSL) Signals for Al 2 O 3 :C and a Readout System with Reproducible Partial Signal Clearance
Optically stimulated luminescent dosimeters (OSLDs) are devices that, when stimulated with light, emit light in proportion to the integrated ionizing radiation dose. The stimulation of OSL material results in the loss of a small fraction of signal stored within the dosimetric traps. Previous studies have investigated the signal loss due to readout stimulation, and the optical annealing of OSLDs. This study builds on former research by examining the behavior of OSL signal after annealing, exploring the functionality of a previously developed signal loss model, and comparing uncertainties for dosimeters reused with or without annealing. For a completely annealed dosimeter, the minimum signal level was 56 ± 8 counts, and readings followed a Gaussian distribution. For dosimeters above this signal level, the fractional signal loss due to the reading process has a linear relationship with the calculated signal. At low signal levels (below 20,000 counts), in this OSLD system,calculated signal percent errors increase significantly but otherwise are on average 0.72 ± 0.27%, 0.40 ± 0.19%, 0.33±0.12%, and 0.24±0.07% for 30, 75, 150, and 300 readings. Theoretical calculations of uncertainties showed that annealing before reusing dosimeters allows for dose errors below 1% with as few as 30 readings. Reusing dosimeters multiple times increases the dose errors especially with low numbers of readouts, so theoretically around 300 readings would be necessary to achieve errors around 1% or below in most scenarios. Note that these dose errors do not include the error associated with the signal-to-dose conversion factor.
61 RADIATION PROTECTION AND DOSIMETRY↗
Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins
Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.
Multi-Agent Reinforcement Learning, Digital Twin, ↗
Robust power detector for wideband signals among many single tone signals
Various technologies for isolating a signal of interest from signals received contemporaneously by an antenna are described herein. A time period for which a signal of interest is present in a second signal can be identified based upon ratios of values of the second signal to the mean value of the second signal. When the ratio of the value of the second signal at a particular time to the mean of the second signal exceeds a threshold value, the signal of interest is considered to be present in the second signal.
47 OTHER INSTRUMENTATION↗
Cytokinin Signaling in Mycobacterium tuberculosis
It was recently reported that the human-exclusive pathogen Mycobacterium tuberculosis secretes cytokinins, which had only been known as plant hormones. While cytokinins are well-established, adenine-based signaling molecules in plants, they have never been shown to participate in signal transduction in other kingdoms of life. M. tuberculosis is not known to interact with plants. Therefore, we tested the hypothesis that cytokinins trigger transcriptional changes within this bacterial species. Here, we show cytokinins induced the strong expression of the M. tuberculosis gene Rv0077c. We found that Rv0077c expression is repressed by a TetR-like transcriptional repressor, Rv0078. Strikingly, cytokinin-induced expression of Rv0077c resulted in a loss of acid-fast staining of M. tuberculosis . Furthermore, while acid-fast staining is thought to be associated with changes in the bacterial cell envelope and virulence, Rv0077c-induced loss of acid-fastness did not affect antibiotic susceptibility or attenuate bacterial growth in mice, consistent with an unaltered mycolic acid profile of Rv0077c-expressing cells. Collectively, these findings show cytokinins signal transcriptional changes that can affect M. tuberculosis acid-fastness and that cytokinin signaling is no longer limited to the kingdom Plantae.
59 BASIC BIOLOGICAL SCIENCES↗
Separating the intrinsic alignment signal and the lensing signal using self-calibration in photo- z surveys with KiDS450 and KV450 Data
ABSTRACT To reach the full potential for the next generation of weak lensing surveys, it is necessary to mitigate the contamination of intrinsic alignments (IAs) of galaxies in the observed cosmic shear signal. The self-calibration (SC) of IAs provides an independent method to measure the IA signal from the survey data and the photometric redshift information. It operates differently from the marginalization method based on the IA modelling. In this work, we present the first application of SC to the KiDS450 data and the KV450 data, to split directly the intrinsic shape–galaxy density (Ig) correlation signal and the gravitational shear–galaxy density (Gg) correlation signal, using the information from photometric redshift (photo-z). We achieved a clear separation of the two signals and performed several validation tests. Our measured signals are found to be in general agreement with the KiDS450 cosmic shear best-fitting cosmology, for both lensing and IA measurements. For KV450, we use partial (high-z) data, and our lensing measurements are also in good agreement with KV450 cosmic shear best fit, while our IA signal suggests a larger IA amplitude for the high-z sample. We discussed the impact of photo-z quality on IA detection and several other potential systematic biases. Finally, we discuss the potential application of the information extracted for both the lensing signal and the IA signal in future surveys.
79 ASTRONOMY AND ASTROPHYSICS↗
Self-configurable radio receiver system and method for use with signals without prior knowledge of signal defining characteristics
A method, radio receiver, and system to autonomously receive and decode a plurality of signals having a variety of signal types without a priori knowledge of the defining characteristics of the signals is disclosed. The radio receiver is capable of receiving a signal of an unknown signal type and, by estimating one or more defining characteristics of the signal, determine the type of signal. The estimated defining characteristic(s) is/are utilized to enable the receiver to determine other defining characteristics. This in turn, enables the receiver, through multiple iterations, to make a maximum-likelihood (ML) estimate for each of the defining characteristics. After the type of signal is determined by its defining characteristics, the receiver selects an appropriate decoder from a plurality of decoders to decode the signal.
Hamkins, Jon↗
Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network
Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.
Kumarasamy, Vijayalakshmi K [The University of Ten↗
Frequency domain laser velocimeter signal processor: A new signal processing scheme
A new scheme for processing signals from laser velocimeter systems is described. The technique utilizes the capabilities of advanced digital electronics to yield a smart instrument that is able to configure itself, based on the characteristics of the input signals, for optimum measurement accuracy. The signal processor is composed of a high-speed 2-bit transient recorder for signal capture and a combination of adaptive digital filters with energy and/or zero crossing detection signal processing. The system is designed to accept signals with frequencies up to 100 MHz with standard deviations up to 20 percent of the average signal frequency. Results from comparative simulation studies indicate measurement accuracies 2.5 times better than with a high-speed burst counter, from signals with as few as 150 photons per burst.
Meyers, James F.↗
Signal Restoration of Non-stationary Acoustic Signals in the Time Domain
Signal restoration is a method of transforming a nonstationary signal acquired by a ground based microphone to an equivalent stationary signal. The benefit of the signal restoration is a simplification of the flight test requirements because it could dispense with the need to acquire acoustic data with another aircraft flying in concert with the rotorcraft. The data quality is also generally improved because the contamination of the signal by the propeller and wind noise is not present. The restoration methodology can also be combined with other data acquisition methods, such as a multiple linear microphone array for further improvement of the test results. The methodology and software are presented for performing the signal restoration in the time domain. The method has no restrictions on flight path geometry or flight regimes. Only requirement is that the aircraft spatial position be known relative to the microphone location and synchronized with the acoustic data. The restoration process assumes that the moving source radiates a stationary signal, which is then transformed into a nonstationary signal by various modulation processes. The restoration contains only the modulation due to the source motion.
Babkin, Alexander S.↗
Signals Getting Crossed in the Entanglement of Redox and Phosphorylation Pathways: Phosphorylation of Peroxiredoxin Proteins Sparks Cell Signaling
Reactive oxygen and nitrogen species have cell signaling properties and are involved in a multitude of processes beyond redox homeostasis. The peroxiredoxin (Prdx) proteins are highly sensitive intracellular peroxidases that can coordinate cell signaling via direct reactive species scavenging or by acting as a redox sensor that enables control of binding partner activity. Oxidation of the peroxidatic cysteine residue of Prdx proteins are the classical post-translational modification that has been recognized to modulate downstream signaling cascades, but increasing evidence supports that dynamic changes to phosphorylation of Prdx proteins is also an important determinant in redox signaling. Phosphorylation of Prdx proteins affects three-dimensional structure and function to coordinate cell proliferation, wound healing, cell fate and lipid signaling. The advent of large proteomic datasets has shown that there are many opportunities to understand further how phosphorylation of Prdx proteins fit into intracellular signaling cascades in normal or malignant cells and that more research is necessary. This review summarizes the Prdx family of proteins and details how post-translational modification by kinases and phosphatases controls intracellular signaling.
59 BASIC BIOLOGICAL SCIENCES↗
Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware
This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
Johnson, Hans↗
Thermodynamics of signal transduction systems and fluctuation theorem in a signal cascade
Biochemical chain reactions are signal transduction cascades that can transmit biological information about the intracellular environment. In this study, we modelled a chain reaction as a code string for applying information theory. Herein, we assumed that cell signal transduction selects a strategy to maximize the transduced signal per signal event duration. To investigate the same, we calculated the information transmission capacity of the reaction chain by maximizing the average entropy production rate per reaction time, indicating the idea of the entropy coding method. Moreover, we defined a signal cascade trajectory. Subsequently, we found that the logarithm of the forward and reverse transition ratio per reaction time is equal to the entropy production rate, which derives the form of the fluctuation theorem in signal transduction. Our findings suggest the application of information entropy theory for analysing signal transduction.
59 BASIC BIOLOGICAL SCIENCES↗