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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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↗
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, ↗
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.↗
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↗
Signal Processing of Multiplexed Optical PWM Signals for Sensor Arrays in Nuclear Environments
Safe and effective generation of terrestrial nuclear power greatly benefits from the actionable data provided by the array of sensors located throughout a plant to provide a holistic online indication of reactor operation. This array includes sensors for monitoring coolant flow and pressure, temperature and heat transfer, radiation levels, structure health monitoring, and other critical parameters for reactor operation. While sensors capable of measuring these parameters have been developed, the electronics used in the pre-amplification and analog-to-digital conversion of the small signals they produce are extremely sensitive and susceptible to damage by high-temperatures and radiation environments nuclear reactors encounter while generating power. The small signals from sensors in nuclear power plants (NPPs) are transmitted over long cable runs which introduce dispersion artifacts into the signals of interest as well as electromagnetic interference (EMI) from lighting fixtures, pumps, mains electricity, and other equipment. To overcome these challenges, a front-end digitization (FREND) platform has been developed to use radiation-tolerant electronics to multiplex, amplify, and optically encode signals from an array of sensors for transmission over an optical fiber to mitigate dispersion and EMI artifacts from long runs of electrical cabling. To recover the optically transmitted data, a signal processing scheme based on 1-dimensional template matching to an indexing channel is described herein and demonstrated to have an effective bit-depth of 9.2 bits (1%). This scheme has been validated in proof-of-concept, non-nuclear testing and preliminary experimental results show good agreement between the measured optical output and and sensor input signals. The FREND system represents a low-loss data link between sensors in nuclear environments and data acquisition hardware which is aimed at improving the signal-to-noise ratio of the data acquired from these sensors to provide better information to operators and researchers.
Sweeney, Dan↗
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 n oise 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.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Methods for the Automated Determination of Sustained Maximum Amplitudes in Oscillating Signals
Machine condition monitoring has been proven to reduce machine downtime and increase productivity. The state-of-the-art research uses vibration monitoring for tasks such as maintenance and tool wear prediction. A less explored aspect is how vibration monitoring might be used to monitor equipment sensitive to vibration. In a manufacturing environment, one example of where this might be needed is in monitoring the vibration of optical linear encoders used in high-precision machine tools and coordinate measuring machines. Monitoring the vibration of sensitive equipment presents a unique case for vibration monitoring because an accurate calculation of the maximum sustained vibration is needed, as opposed to extracting trends from the data. To do this, techniques for determining sustained peaks in vibration signals are needed. Here, this work fills this gap by formalizing and testing methods for determining sustained vibration amplitudes. The methods are tested on simulated signals based on experimental data. Results show that processing the signal directly with the novel Expire Timer method produces the smallest amounts of error on average under various test conditions. Additionally, this method can operate in real-time on streaming vibration data.
Industrial Internet of Things↗
Differential regulation of G protein signaling in Arabidopsis through two distinct pathways that internalize AtRGS1
Segregated pathways enable AtRGS1 endocytosis to transduce signal-specific responses in Arabidopsis .
Biochemistry & Molecular Biology↗
Chloroplast Stress Signals: Control of Retrograde Signaling, Chloroplast Turn-Over, and Cell Fate Decisions
Chloroplasts (photosynthetic plastids) are semiautonomous organelles that contain their own small genomes. The proteomes of chloroplasts, however, are a mixture of plastid and nuclear-encoded proteins. Chloroplasts perform photosynthesis, which is prone to damaging the organelles, leading to the production of reactive oxygen species (ROS) that damage the cell under environmental stresses. Thus, for the cell to maintain proper chloroplast function, efficient photosynthesis, and avoid ROS damage, it relies on complex crosstalk between the chloroplast, the nucleus, other organelles within the cell, and the cytoplasm in between. This communication involves retrograde signals from chloroplasts to control nuclear gene expression, programmed cell death (PCD), and chloroplast degradation. Here we review these signals with an emphasis on the roles of the ROS singlet oxygen ( 1 O 2 ) and plastid gene expression. We cover (1) recent work on understanding how multiple 1 O 2 signaling pathways can be initiated within stressed chloroplasts, (2) how individualized post-translational regulatory systems allow chloroplasts to control their proteomes and degradation, and (3) how chloroplast signals ultimately control cell fate decisions, such as PCD, senescence, and vacuole-mediated degradation of chloroplasts (chloroplast quality control). Overall, this chapter discusses how chloroplasts can act as environmental sensors for the cell and allow plants to acclimate to stress and thrive in dynamic environments.
59 BASIC BIOLOGICAL SCIENCES↗
Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach
Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.
adaptive LQR control↗