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Algorithmic approach is useful for analysis of systems with noisy inputs and when outputs are required in terms of statistical quantities and probabilities of signal excursions beyond desired levels. Procedure can be used with circuits, chemical processes, material design, and other systems with known transfer functions and parameter tolerances.
For applications to the problem of radio frequency interference identification, or in the search for extraterrestrial intelligence, it is important to have a basic understanding of signal detection algorithms. A general technique for assessing the asymptotic sensitivity of a broad class of signal detection algorithms is given. In these algorithms, the decision is based on the value of X sub 1 + X sub 2...+ X sub n where the X sub 1's are obtained by sampling and preliminary processing of a physical process.
Electric potentials and magnetic fields generated by ensembles of synchronously active neurons in response to external stimuli provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult as each detector records signals simultaneously generated by various regions throughout the brain. We introduce the differentially Variable Component Analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we evaluate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. Finally, we evaluate the technique using visually evoked field potentials recorded at incremental depths across the layers of cortical area VI, in an awake, behaving macaque monkey.
The Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3) is the third of the NASA AVIRIS spectrometer series and is being developed in parallel with the Compact Wide-swath Imaging Spectrometer II (CWIS-II) for the University of Zurich, Switzerland. The core spectrometer of AVIRIS-3 is a copy of the optically fast, F/1.8 Dyson imaging spectrometer used by the Earth Surface Mineral Dust Source Investigation (EMIT) that is in development and scheduled for launch to the International Space Station (ISS) in 2022. AVIRIS-3 is intended to provide state-of-the-art imaging spectroscopy measurements for NASA science and application through the next decade and beyond. AVIRIS-3 uses the EMIT spectrometer design interfaced with a scaled two mirror telescope enclosed in a portable vacuum vessel to enable measurements from airborne platforms ranging from a Twin Otter to a business jet or a NASA ER-2. AVIRIS-3 is a cryogenic instrument with advanced system control and real-time onboard spectroscopic data processing algorithms evolved from AVIRIS-NG. The spectral range of AVIRIS-3 is 380 to 2500 nm with 7.4 nm sampling. The radiometric range is from 0 to max terrestrial Lambertian radiance with higher signal-to-noise ratio performance than AVIRIS-Classic or AVIRIS-Next Generation. The spatial field-of-view is 39.5 degrees with 0.56 milliradian sampling. This paper describes the design and development of AVIRIS-3 and presents its characteristics in comparison to the previous generation imaging spectrometers.
Purpose: The purpose of the study is to determine whether a novel semi-automated DIXON-based fat quantification algorithm can reliably quantify visceral fat using a CT-based reference standard. Methods: This was an IRB-approved retrospective cohort study of 27 subjects who underwent abdominopelvic CT within 7 days of proton density fat fraction (PDFF) mapping on a 1.5T MRI. Cross-sectional visceral fat area per slice (cm{sup 2}) was measured in blinded fashion in each modality at intervertebral disc levels from T12 to L4. CT estimates were obtained using a previously published semi-automated computational image processing system that sums pixels with attenuation − 205 to − 51 HU. MR estimates were obtained using two novel semi-automated DIXON-based fat quantification algorithms that measure visceral fat area by spatially regularizing non-uniform fat-only signal intensity or de-speckling PDFF 2D images and summing pixels with PDFF ≥ 50%. Pearson’s correlations and Bland–Altman analyses were performed. Results: Visceral fat area per slice ranged from 9.2 to 429.8 cm{sup 2} for MR and from 1.6 to 405.5 cm{sup 2} for CT. There was a strong correlation between CT and MR methods in measured visceral fat area across all studied vertebral body levels (r = 0.97; n = 101 observations); the least (r = 0.93) correlation was at T12. Bland–Altman analysis revealed a bias of 31.7 cm{sup 2} (95% CI [− 27.1]–90.4 cm{sup 2}), indicating modestly higher visceral fat assessed by MR. Conclusion: MR- and CT-based visceral fat quantification are highly correlated and have good cross-modality reliability, indicating that visceral fat quantification by either method can yield a stable and reliable biomarker.
The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is often unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.
We compare different algorithms for detecting a 5 cm tungsten cube using cosmic ray muon technology. In each case, a simple tomographic technique was used for position reconstruction, but the scattering angles were used differently to obtain a density signal. Receiver operating characteristic curves were used to compare images made using average angle squared, median angle squared, average of the squared angle, and a multi-energy group fit of the angular distributions for scenes with and without a 5 cm tungsten cube. The receiver operating characteristic curves show that the multi-energy group treatment of the scattering angle distributions is the superior method for image reconstruction.
The ability of the autonomic nervous system to alter the QT interval in response to heart rate changes is essential to cardiovascular control. An accurate way to determine the relation between QT intervals and their corresponding RR intervals is described. A computer algorithm measures the RR intervals using digital filtering and cross-correlating the QRS sections of consecutive waveforms. The QT intervals is calculated by choosing a section of, the ECG that includes the T wave and cross-correlating it with all the consecutive T waves. At least 4000 pairs of QT-RR intervals are computed for each subject and a best fit correlation function determines the relations between the QT and RR intervals. This technique enables to establish a precise correlation between RR and QT in order to distinguish between control and SIDS babies.
Complex samples require multidimensional measurements with high resolution for full characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometry (IMS-Orbitrap MS) platform. To circumvent the timing difference between the fast IMS separation and the slow Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequence to multiplex ions into the drift tube and Orbitrap. The instrument was designed to operate in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to fully optimize the signal-to-ratio of the measurements. For the SM measurements, a previously developed algorithm was used to reconstruct the IMS data, while a new algorithm was developed for the DM analyses. The new algorithm is a two-step process that first recovers the SM data from the encoded DM data and then decoded the SM data. The algorithm also performs multiple refining procedures in order to minimize the demultiplexing artifacts traditionally observed in such scheme. The new IMS-Orbitrap MS platform was demonstrated for the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.
Abstract The Continuous Electron Beam Accelerator Facility (CEBAF) energy upgrade from 6 GeV to 12 GeV includes the installation of four new 748.5 MHz normal conducting deflecting cavities in the 5th pass extraction region. The RF system employs two digital LLRF systems controlling four normal conducting cavities in a vector sum setting. Cavity tune information of the individual cavities is obtained using a multiplexing scheme of the forward and reflected RF signals. Water skids equipped with heaters and valves are used to control resonance. A new FPGA-based hardware and EPICS-based predictive control algorithm has been developed to support reliable operation of the beam extraction process. This paper presents the architecture design of the existing hardware and software as well as a plan to develop a model predictive control system.
In this work, we describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes aimed at the broad astrophysics community. Y3 GOLD improves on previous releases from DES, Y1 GOLD, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 GOLD comprises nearly 5000 deg 2 of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ~10 for extended objects up to iAB ~ 23.0, and top-of-the-atmosphere photometric uniformity <3 mmag. Compared to DR1, photometric residuals with respect to Gaia are reduced by 50%, and per-object chromatic corrections are introduced. Y3 GOLD augments DES DR1 with simultaneous fits to multi-epoch photometry for more robust galactic color measurements and corresponding photometric redshift estimates. Y3 GOLD features improved morphological star–galaxy classification with efficiency >98% and purity >99% for galaxies with 19 < i AB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis samples.
It is pointed out that the two-dimensional cyclic convolution is a useful tool for many two-dimensional digital signal processing applications. Two important applications are related to spaceborne high-resolution synthetic aperture radar (SAR) processing and image processing. Nussbaumer and Quandalle (1978) showed that a radix-2 polynomial transform analogous to the conventional radix-2 FFT algorithm can be used to compute a two-dimensional cyclic convolution. On the basis of results reported by Arambepola and Rayner (1979), a radix-2 polynomial transform can be defined to compute a multidimensional cyclic convolution. Truong et al. (1981) used the considered ideas together with the Chinese Theorem to further reduce the complexity of the radix-2 fast polynomial transform (FPT). Reed et al. (1981) demonstrated that such a new FPT algorithm is significantly faster than the FFT algorithm for computing a two-dimensional convolution. In the present investigation, a parallel-pipeline architecture is considered for implementing the FPT developed by Truong et al.
Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.
One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.
SAND2025-00659O MAPLE is a software tool that uses epigenomic data to predict gene expression. MAPLE uses a set of epigenomic modifications to determine the effect on gene expression in a specific subset of species. The algorithm can be trained on additional species and epigenomic modifications, enhancing its predictive capabilities. EAGLE employs a hybrid neural network architecture, featuring a convolutional front-end and a multi-head attention layer, to process pre-processed signal data as input. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
The NASA NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. This volume addresses the impact of data set selection on data formatting required for efficient telemetering of the acquired satellite sensor data. More specifically, the FILE algorithm developed by Martin-Marietta provides a means for the determination of those pixels from the data stream effects an improvement in the achievable system throughput. It will be seen that based on the lack of statistical stationarity in cloud cover, spatial distribution periods exist where data acquisition rates exceed the throughput capability. The study therefore addresses various approaches to data compression and truncation as applicable to this sensor mission.
Several on line and off line data processing techniques are used to remove interfering signals due to ground clutter, aircraft, instrumental effects, and external transmissions from the desired atmospheric echoes of Mesosphere Stratosphere, Troposphere (MST) radar. The on line, real time techniques are necessarily simple in order to minimize processing delays. This algorithm examines the individual Doppler spectra which are computed every two to four seconds (for oblique antenna beams). The total spectral power in each individual spectrum is computed by summing all the spectral points. If this integrated power increases from one spectrum to the next by a factor greater than a preselected threshold, then that spectrum is not added to the spectral sum. Succeeding spectra are compared to the last acceptable spectrum. Only a certain maximum number of spectra are allowed to be rejected in succession.
Imaging Venus with a synthetic aperture radar (SAR) with 70 percent global coverage at 1-km optical line-pair resolution to provide a detailed global characterization of the volcanic land-forms on Venus by an integration of image data with altimetry is discussed. The Magellan radar system uses navigation predictions to preset the radar data collection parameters. The data are collected in such a way as to preserve the Doppler signature of surface elements and later they are transmitted to the earth for processing into high-resolution radar images. To maintain high accuracy, a complex on-board filter algorithm allows the altitude control logic to respond only to a narrow range of expected photon intensity levels and only to signals that occur within a small predicted interval of time. Each mapping pass images a swath of the planet that varies in width from 20 to 25 km. Since the orbital plane of the spacecraft remains fixed in the inertial space, the slow rotation of Venus continually brings new areas into view of the spacecraft.