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The design and performance of a prototype real-time baseband signal combiner, used to enhance the received Voyager 2 spacecraft signals during the Jupiter flyby, is described. Hardware delay paths, operating programs, and firmware are discussed.
Optical-communication receivers of proposed type implement high-sensitivity scheme of optical parametric amplification followed by direct detection for reception of extremely weak signals. Incorporates both optical parametric amplification and direct detection into optimized design enhancing effective signal-to-noise ratios during reception in photon-starved (photon-counting) regime. Eliminates need for complexity of heterodyne detection scheme and partly overcomes limitations imposed on older direct-detection schemes by noise generated in receivers and by limits on quantum efficiencies of photodetectors.
In the preferred embodiment, an encrypted GPS signal is down-converted from RF to baseband to generate two quadrature components for each RF signal (L1 and L2). Separately and independently for each RF signal and each quadrature component, the four down-converted signals are counter-rotated with a respective model phase, correlated with a respective model P code, and then successively summed and dumped over presum intervals substantially coincident with chips of the respective encryption code. Without knowledge of the encryption-code signs, the effect of encryption-code sign flips is then substantially reduced by selected combinations of the resulting presums between associated quadrature components for each RF signal, separately and independently for the L1 and L2 signals. The resulting combined presums are then summed and dumped over longer intervals and further processed to extract amplitude, phase and delay for each RF signal. Precision of the resulting phase and delay values is approximately four times better than that obtained from straight cross-correlation of L1 and L2. This improved method provides the following options: separate and independent tracking of the L1-Y and L2-Y channels; separate and independent measurement of amplitude, phase and delay L1-Y channel; and removal of the half-cycle ambiguity in L1-Y and L2-Y carrier phase.
Systems and methods for high-speed compression of dynamic electrical signal waveforms to extend the measuring capabilities of conventional measuring devices such as oscilloscopes and high-speed data acquisition systems are discussed. Transfer function components and algorithmic transfer functions can be used to accurately measure signals that are within the frequency bandwidth but beyond the voltage range and voltage resolution capabilities of the measuring device.
Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.
Application of digital signal processing techniques to improve the non-linear dynamic characteristics of a sonar-type mercury manometer is described. The dynamic enhancement strategy quasi-linearizes the manometer characteristics and improves the effective bandwidth in the context of a wind-tunnel pressure regulation system. Model identification data and real-time hybrid simulation data demonstrate feasibility of approach.
A new technique for measuring cloud liquid water, mean droplet radius and droplet number density is outlined. The technique is based on simultaneously measuring Raman and Mie scattering from cloud liquid droplets using a Raman lidar. Laboratory experiments on liquid micro-spheres have shown that the intensity of Raman scattering is proportional to the amount of liquid present in the spheres. This fact is used as a constraint on calculated Mie intensity assuming a gamma function particle size distribution. The resulting retrieval technique is shown to give stable solutions with no false minima. It is tested using Raman lidar data where the liquid water signal was seen as an enhancement to the water vapor signal. The general relationship of retrieved average radius and number density is consistent with traditional cloud physics models. Sensitivity to the assumed maximum cloud liquid water amount and the water vapor mixing ratio calibration are tested. Improvements to the technique are suggested.
A high sensitivity laser interferometry for continuous monitoring of optical path changes is presented. The sensitivity to small changes in the optical path is achieved by means of a feedback system which keeps the interferometer at its maximum (and linear) position. Two experiments were designed to verify the interferometer's performance, both in direct and in scattering configurations. In each case, small temperature fluctuations were introduced as the signal source. Simultaneous measurement of temperature were compared with the results of the interferometer. Good correlation between the results were obtained. Finally, the application of the high sensitivity interferometer for detection of boundary-layer transition is examined. Optical signal strength and possible sources of noise are examined. Some methods of model surface preparation for enhancement of signal-to-noise ratio are discussed.
Osteoblasts respond to both fluid shear and parathyroid hormone (PTH) with a rapid increase in intracellular calcium concentration ([Ca2+]i). Because both stimuli modulate the kinetics of the mechanosensitive cation channel (MSCC), we postulated PTH would enhance the [Ca2+]i response to fluid shear by increasing the sensitivity of MSCCs. After a 3-minute preflow at 1 dyne/cm2, MC3T3-E1 cells were subjected to various levels of shear and changes in [Ca2+]i were assessed using Fura-2. Pretreatment with 50 nM bovine PTH(1-34) [bPTH(1-34)] significantly enhanced the shear magnitude-dependent increase in [Ca2+]i. Gadolinium (Gd3+), an MSCC blocker, significantly inhibited the mean peak [Ca2+]i response to shear and shear + bPTH(1-34). Nifedipine (Nif), an L-type voltage-sensitive Ca2+ channel (VSCC) blocker, also significantly reduced the [Ca2+]i response to shear + bPTH(1-34), but not to shear alone, suggesting VSCC activation plays an interactive role in the action of these stimuli together. Activation of either the protein kinase C (PKC) or protein kinase A (PKA) pathways with specific agonists indicated that PKC activation did not alter the Ca2+ response to shear, whereas PKA activation significantly increased the [Ca2+]i response to lower magnitudes of shear. bPTH(1-34), which activates both pathways, induced the greatest [Ca2+]i response at each level of shear, suggesting an interaction of these pathways in this response. These data indicate that PTH significantly enhances the [Ca2+]i response to shear primarily via PKA modulation of the MSCC and VSCC.
Reactive health monitoring strategies during events like the COVID-19 pandemic highlighted the need for predictive, threat-agnostic diagnostics that can detect both known diseases and novel chemical or biological threats. To address this, we investigated an optical biosensor as a breath volatile organic compound (VOC) analyzer, aiming to emulate biological olfaction. We assembled and validated the device with thin film metal coated substrate-based sensors. We immobilized small biological recognition elements on the substrates and delivered controlled concentrations of target VOCs. The sensor was irradiated with a visible laser and the sensor signal was recorded. We characterized the laser performance and tested 3 recognition elements for 2 VOCs with varying concentrations (1-100 ppm). We also evaluated enhancement of the signal using nanostructures on the metal film in comparison with planar film substrate. We demonstrated detecting ethanol reliably at concentrations as low as ~2 ppm along with preliminary detection of acetone (<100 ppm). We also found several unexpected factors that influence the sensor behavior that should be addressed to further refine the device’s performance. The nanostructures were, as expected, found to amplify the sensor signals. These findings demonstrate the feasibility of the optical bio-sensing modality for breath VOC monitoring at physiologically relevant levels. This positions LLNL to develop a low-cost, scalable, broad-spectrum health monitoring capability aligned with the Early Detection thrust of the Bioresilience Mission Focus Area and attract external funding.
The robustness of an atomic fountain interferometer with respect to variations in the initial velocity of the atoms and deviations from the optimal pulse amplitude is examined. We numerically simulate the dynamics of an interferometer in momentum space with a maximum separation of and map out the expected signal contrast depending on the variance of the initial velocity distribution and the value of the laser field amplitude. We show that an excitation scheme based on rapid adiabatic passage significantly enhances the expected signal contrast, compared to the commonly used scheme consisting of a series of and pulses. We demonstrate further substantial increase of the robustness by using optimal control theory to identify splitting and swapping pulses that perform well on an ensemble average of pulse amplitudes and velocities. Our results demonstrate the ability of optimal control to significantly enhance future implementations of atomic fountain interferometry.
The Radio Plasma Imager (RPI) on the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) mission operates like a radar by transmitting and receiving coherent electromagnetic pulses. Long-range echoes of electromagnetic sounder waves are reflected at remote plasma cutoffs. Thus, analyses of RPI observations will yield the plasma parameters and distances to the remote reflection points. These analyses assume that the reflecting plasma surfaces are cold and are sufficiently smooth that they effectively behave as plane mirrors to the incoming sounder waves, i.e., that geometric optics can be used. The RPI will employ pulse compression and spectral integration techniques, perfected in ground-based ionospheric digital sounders, in order to enhance the signal-to-noise ratio in long-range magnetospheric sounding. When plasma irregularities exist in the remote magnetospheric plasmas that are being probed by the sounder waves, echo signatures may become complicated. Ionospheric sounding experience indicates that while topside sounding echo strengths can actually be enhanced by the presence of irregularities, ground-based sounding indicates that coherent detection techniques can still be employed. In this paper we investigate the plasma conditions that will allow coherent signals to be detected by the RPI and the signatures to be expected, such as scattering and plasma resonances, in the presence of multi-scale irregularities, may possibly have on RPI signals. Sounding of irregular plasma structures in the plasmasphere, plasmapause and magnetopause are also discussed.
The process of combining telemetry signals received at multiple antennas, commonly referred to as arraying, can be used to improve communication link performance in the Deep Space Network (DSN). By coherently adding telemetry from multiple receiving sites, arraying produces an enhancement in signal-to-noise ratio (SNR) over that achievable with any single antenna in the array. A number of different techniques for arraying have been proposed and their performances analyzed in past literature. These analyses have compared different arraying schemes under the assumption that the signals contain additive white Gaussian noise (AWGN) and that the noise observed at distinct antennas is independent. In situations where an unwanted background body is visible to multiple antennas in the array, however, the assumption of independent noises is no longer applicable. A planet with significant radiation emissions in the frequency band of interest can be one such source of correlated noise. For example, during much of Galileo's tour of Jupiter, the planet will contribute significantly to the total system noise at various ground stations. This article analyzes the effects of correlated noise on two arraying schemes currently being considered for DSN applications: full-spectrum combining (FSC) and complex-symbol combining (CSC). A framework is presented for characterizing the correlated noise based on physical parameters, and the impact of the noise correlation on the array performance is assessed for each scheme.
The effects of shifts in the rate of presentation of repetitive neutral events (background event rate) were studied in a visual vigilance task. Four groups of subjects experienced either a high (21 events/min) or a low (6 events/min) event rate for 20 min and then experienced either the same or the alternate event rate for an additional 40 min. The temporal occurrence of critical target signals was identical for all groups, irrespective of event rate. The density of critical signals was 12 signals/20 min. By the end of the session, shifts in event rate were associated with changes in performance which resembled contrast effects found in other experimental situations in which shift paradigms were used. Relative to constant event rate control conditions, a shift from a low to a high event rate depressed the probability of signal detections, while a shift in the opposite direction enhanced the probability of signal detections.
This paper presents a small-signal stability analysis of grid-forming (GFM) inverters under current-limiting conditions. It examines how adjustments in virtual impedance angles, implemented through advanced current-limiting and frequency stabilization techniques, influence small-signal stability. This paper studies a GFM inverter control integrating a fictitious power technique stabilizing primary control by adding a virtual power term and a hybrid current limiter integrating virtual impedance in the anti-wind-up feedback with current reference saturation limiting. A small-signal model is developed to assess the impact of virtual impedance angles on GFM inverter dynamics during grid disturbances, such as voltage drops. The findings indicate that although increasing the virtual impedance angle (to make it more inductive) enhances large-signal stability and voltage support during faults, it can induce oscillations and lead to instability if the angle exceeds certain thresholds. Based on the small-signal models, this paper provides design considerations for the current-limiter impedances to ensure reliable GFM inverter behavior under grid disturbances while maintaining small-signal stability.
The present disclosure pertains to metal or dielectric nanostructures of the subwavelength scale within the grating lines of optical diffraction gratings. The nanostructures have surface plasmon resonances or non-plasmon optical resonances. A linear photodetector array is used to capture the resonance spectra from one of the diffraction orders. The combined nanostructure super-grating and photodetector array eliminates the use of external optical spectrometers for measuring surface plasmon or optical resonance frequency shift caused by the presence of chemical and biological agents. The nanostructure super-gratings can be used for building integrated surface enhanced Raman scattering (SERS) spectrometers. The nanostructures within the diffraction grating lines enhance Raman scattering signal light while the diffraction grating pattern of the nanostructures diffracts Raman scattering light to different directions of propagation according to their wavelengths. Therefore, the nanostructure super-gratings allows for the use of a photodetector array to capture the surface enhanced Raman scattering spectra.
The present disclosure pertains to metal or dielectric nanostructures of the subwavelength scale within the grating lines of optical diffraction gratings. The nanostructures have surface plasmon resonances or non-plasmon optical resonances. A linear photodetector array is used to capture the resonance spectra from one of the diffraction orders. The combined nanostructure super-grating and photodetector array eliminates the use of external optical spectrometers for measuring surface plasmon or optical resonance frequency shift caused by the presence of chemical and biological agents. The nanostructure super-gratings can be used for building integrated surface enhanced Raman scattering (SERS) spectrometers. The nanostructures within the diffraction grating lines enhance Raman scattering signal light while the diffraction grating pattern of the nanostructures diffracts Raman scattering light to different directions of propagation according to their wavelengths. Therefore, the nanostructure super-gratings allows for the use of a photodetector array to capture the surface enhanced Raman scattering spectra.
Mathematical deconvolution of digitized analog signals from scientific measuring instruments is shown to be a means of extracting important information which is otherwise hidden due to time-constant and other broadening or distortion effects caused by the experiment. Three different approaches to deconvolution and their subsequent application to recorded data from three analytical instruments are considered. To demonstrate the efficacy of deconvolution, the use of these approaches to solve the convolution integral for the gas chromatograph, magnetic mass spectrometer, and the time-of-flight mass spectrometer are described. Other possible applications of these types of numerical treatment of data to yield superior results from analog signals of the physical parameters normally measured in aerospace simulation facilities are suggested and briefly discussed.