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DOE OSTI2021

Preface to the Special Issue on Signal Processing, Data Acquisition and Front-end Systems for Radiation Detection and Measurements

This special issue of the Journal of Signal Processing Systems focuses on advancement of signal processing and data-acquisition for radiation detection and measurements. The seven articles included cover diverse topics within the scope of radiation detection ranging from understanding of fundamental fission process, instrumentation, and application of radiation interaction (e.g., tomography and imaging). The articles focus on signal processing for fission spectrometer, neutron and gamma-ray computed tomography and artificial intelligence methods, dynamic neutron imaging system, pulse-pileup correction in x-ray spectroscopy and calibration methods in radiation measurement.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1975

Signal processing and display interface studies

Signal processing equipment specifications, operating and test procedures, and systems design and engineering are described. Five subdivisions of the overall circuitry are treated: (1) the spectrum analyzer; (2) the spectrum integrator; (3) the velocity discriminator; (4) the display interface; and (5) the formatter. They function in series: (1) first in analog form to provide frequency resolution, (2) then in digital form to achieve signal to noise improvement (video integration) and frequency discrimination, and (3) finally in analog form again for the purpose of real-time display of the significant velocity data. The formatter collects binary data from various points in the processor and provides a serial output for bi-phase recording. Block diagrams are used to illustrate the system.

Source record↗

NASA NTRS1982

The application of digital signal processing techniques to a teleoperator radar system

A digital signal processing system was studied for the determination of the spectral frequency distribution of echo signals from a teleoperator radar system. The system consisted of a sample and hold circuit, an analog to digital converter, a digital filter, and a Fast Fourier Transform. The system is interfaced to a 16 bit microprocessor. The microprocessor is programmed to control the complete digital signal processing. The digital filtering and Fast Fourier Transform functions are implemented by a S2815 digital filter/utility peripheral chip and a S2814A Fast Fourier Transform chip. The S2815 initially simulates a low-pass Butterworth filter with later expansion to complete filter circuit (bandpass and highpass) synthesizing.

Pujol, A.↗

NASA NTRS1995

Signal processing for ION mobility spectrometers

Signal processing techniques for systems based upon Ion Mobility Spectrometry will be discussed in the light of 10 years of experience in the design of real-time IMS. Among the topics to be covered are compensation techniques for variations in the number density of the gas - the use of an internal standard (a reference peak) or pressure and temperature sensors. Sources of noise and methods for noise reduction will be discussed together with resolution limitations and the ability of deconvolution techniques to improve resolving power. The use of neural networks (either by themselves or as a component part of a processing system) will be reviewed.

Taylor, S.↗

DOE OSTI2026

Exact chiral symmetry with quantum signal processing

We give a quantum signal processing (QSP) algorithm for the overlap fermion Hamiltonian which preserves the Ginsparg-Wilson relation up to a controllable error $ε_e$. Quantum simulations of Dirac fermions with exact chiral symmetry are thus nearly free: applying the overlap Hamiltonian costs only a factor logarithmic in $ε_e$ more than the Wilson-Dirac Hamiltonian. Comparing to domain-wall fermions, a mild overhead is found in circuit complexity while reducing qubit costs. We show how QSP effectively constructs an extra dimension when simulating the overlap operator, illustrating that the scaling of quantum algorithms reflects the deeper physics of overlap fermions arising at the boundary of domain-wall fermions.

Lamm, Henry [Fermilab] (ORCID:0000000330330791)↗

NASA NTRS1985

Application of homomorphic signal processing to stress wave factor analysis

The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel p times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with p, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.

Karagulle, H.↗

NASA NTRS1985

Application of homomorphic signal processing to stress wave factor analysis

The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel P times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with P, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.

Williams, J. H., Jr.↗

NASA NTRS2003

Parallel Signal Processing and System Simulation using aCe

Recently, networked and cluster computation have become very popular for both signal processing and system simulation. A new language is ideally suited for parallel signal processing applications and system simulation since it allows the programmer to explicitly express the computations that can be performed concurrently. In addition, the new C based parallel language (ace C) for architecture-adaptive programming allows programmers to implement algorithms and system simulation applications on parallel architectures by providing them with the assurance that future parallel architectures will be able to run their applications with a minimum of modification. In this paper, we will focus on some fundamental features of ace C and present a signal processing application (FFT).

Dorband, John E.↗

NASA NTRS2013

Advanced Signal Processing for High Temperatures Health Monitoring of Condensed Water Height in Steam Pipes

An advanced signal processing methodology is being developed to monitor the height of condensed water thru the wall of a steel pipe while operating at temperatures as high as 250deg. Using existing techniques, previous study indicated that, when the water height is low or there is disturbance in the environment, the predicted water height may not be accurate. In recent years, the use of the autocorrelation and envelope techniques in the signal processing has been demonstrated to be a very useful tool for practical applications. In this paper, various signal processing techniques including the auto correlation, Hilbert transform, and the Shannon Energy Envelope methods were studied and implemented to determine the water height in the steam pipe. The results have shown that the developed method provides a good capability for monitoring the height in the regular conditions. An alternative solution for shallow water or no water conditions based on a developed hybrid method based on Hilbert transform (HT) with a high pass filter and using the optimized windowing technique is suggested. Further development of the reported methods would provide a powerful tool for the identification of the disturbances of water height inside the pipe.

health monitoring↗

DOE OSTI2026

Signal Processing in SBND with Calibrated and Validated Electronics and Field Responses

SBND is a liquid argon time projection chamber in Fermilab’s Short-Baseline Neutrino Program, located 110 m from the neutrino source and operating in a high-rate environment with unprecedented statistics. Charged particles from neutrino interactions ionize the argon, and the resulting electrons drift to the anode wires, inducing current signals recorded as raw waveforms. These waveforms are a convolution of deposited charge with the electronics and TPC field responses, making accurate signal processing essential for recovering the true charge distribution. Signal processing forms the starting point for SBND reconstruction, directly impacting hit finding, charge calibration, clustering, and the reconstruction of tracks and showers, and therefore playing a key role in energy reconstruction and particle identification. In this poster, we present an overview of the SBND signal processing chain, including noise removal, channel-by-channel electronics correction, signal identification, and deconvolution using measured electronics and TPC field responses. We demonstrate that the two kernel functions—electronics and field responses—achieve high precision when compared to data, ensuring that the SBND signal processing chain provides a robust and accurate foundation for event reconstruction and precision physics measurements.

Singh, Prabhjot [Louisiana State U.] (ORCID:000000↗

NASA NTRS1989

Signal processing applied to photothermal techniques for materials characterization

There is a need to make noncontact measurements of material characteristics in the microgravity environment. Photothermal and photoacoustics techniques offer one approach for attaining this capability since lasers can be used to generate the required thermal or acoustic signals. The perturbations in the materials that can be used for characterization can be detected by optical reflectance, infrared detection or laser detection of photoacoustics. However, some of these laser techniques have disadvantages of either high energy pulsed excitation or low signal-to-noise ratio. Alternative signal processing techniques that have been developed can be applied to photothermal or photoacoustic instrumentation. One fully coherent spread spectrum signal processing technique is called time delay spectrometry (TDS). With TDS the system is excited using a combined frequency-time domain by employing a linear frequency sweep excitation function. The processed received signal can provide either frequency, phase or improved time resolution. This signal processing technique was shown to outperform other time selective techniques with respect to noise rejection and was recently applied to photothermal instrumentation. The technique yields the mathematical equivalent of pulses yet the input irradiances are orders of magnitude less than pulses with the concomitant reduction in perturbation of the sample and can increase the capability of photothermal methods for materials characterization.

Rooney, James A.↗

NASA NTRSDate not supplied

Navigation Doppler Lidar Signal Processing Architecture and Algorithms

A coherent Doppler lidar has been developed to address the need for a high-performance, compact, and cost-effective velocity and altitude sensor onboard landing vehicles. One of the core subsystems within NDL is its signal processing architecture. The primary function of this subsystem is to digitize the analog output of the homodyned signal received from the photo-diode associated with each telescope, process this signal in the frequency domain, and then turn this information into velocity and range measurements. A secondary function of this subsystem is to setup and control the internal components within NDL and to enable the instrument to interface with external systems by receiving commands, sending telemetry, and synchronizing its measurements with external clocks and pulses. This paper will give an overview of the signal processing architecture within NDL. This will include a discussion on its fundamental processing components and the core signal processing algorithms. Command, telemetry and internal component controls will also be discussed as will how the signal processing architecture has also evolved over the years of development. We will discuss provisions for robust operation including Triple-Mode Redundancy (TMR) within NDL’s Field Programmable Gate Array (FPGA) and storage elements, a fault-tolerant boot process, and Error Detection and Correction (EDAC) on the Static Random-Access Memories (SRAMs). Finally, we conclude this paper with how this architecture could continue to evolve.

Doppler Lidar↗

DOE OSTI2013

Nonlinear Signal Processing for Removing Microphonic Noise from Nuclear Spectrometer Measurements: Sparse Linear Modeling via L1 Norm Regularization [Slides]

The focus of this initial work is to apply recent breakthroughs in sparse linear modeling and nonlinear signal processing to the problem of removing microphonic noise from nuclear spectrum measurements. The resolution of nuclear spectrum measurements is commonly degraded by microphonic noise caused by the vibration of supporting equipment (i.e. cryocoolers) as well as motion of the detector itself in mobile applications. Current signal processing techniques cannot remove microphonic noise without modifying the desired characteristics of the spikes induced by nuclear detection events. The LANL Engineering Institute has been working with NEN‐1 to remedy this problem. Initial work focused on developing active control systems to cancel the vibrations experienced by nuclear detectors. As work on progressed along these lines the team realized that a more robust, lower‐energy, lower‐cost solution to the microphonic noise problem could be realized using recent breakthroughs in nonlinear signal processing. Over the course of the last decade significant advances have been made in the field of sparse linear modeling using L1 norm minimization to find linear representations of sparse signals. The problem of removing microphonic noise from nuclear detection measurements appears to be well suited to the L1 minimization approach because these measurements tend to be inherently sparse in two different domains. The nuclear detections are sparse in a spike domain, and the microphonic noise is sparse in the Fourier basis. L1 minimization allows us to decompose the measurements into these two domains. The result is that we can potentially separate the spikes from the microphonic noise. Initial investigations into this technique look very promising. In this work microphonic noise is induced in an HE‐3 detector by a wrench impact. A pulser is used to generate uniform, synthetic, nuclear events and is fed into the detector front end electronics. Nearly all the microphonic noise is separated from the spike measurements. The histograms in Figure 2 illustrate that spectrum widening caused by noise is significantly reduced. The goal of future work to further develop this technique to account for variations in spike characteristics, as well as to use adaptive signal processing and modern Graphical Processing Unit (GPU) technology to speed up the signal decompositions.

42 ENGINEERING↗

NASA NTRS1965

The distribution of the outputs from a filter band signal processing system

Distribution of outputs from filter band signal processing system

OUTPUT↗

NASA NTRS2004

Signal Processing Variables for Optimization of Flaw Detection in Composites Using Ultrasonic Guided Wave Scanning

This study analyzes the effect of signal processing variables on the ability of the ultrasonic guided wave scan method at NASA Glenn Research Center to distinguish various flaw conditions in ceramic matrix composites samples. In the ultrasonic guided wave scan method, several time- and frequency-domain parameters are calculated from the ultrasonic guided wave signal at each scan location to form images. The parameters include power spectral density, centroid mean time, total energy (zeroth moment), centroid frequency, and ultrasonic decay rate. A number of signal processing variables are available to the user when calculating these parameters. These signal processing variables include 1) the time portion of the time-domain waveform processed, 2) integration type for the properties requiring integrations, 3) bounded versus unbounded integrations, 4) power spectral density window type, 5) and the number of time segments chosen if using the short-time fourier transform to calculate ultrasonic decay rate. Flaw conditions examined included delamination, cracking, and density variation.

Roth, Don J.↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

This report presents the research, design, and implementation of a Toroid Signal Processing Container with Redis Integration, conducted during my internship assignment. The project focused on developing a modular signal processing sys- tem capable of performing baseline correction, droop compensation, and real- time data communication for beam diagnostic signals. Using Redis as a message broker and configuration manager, the system ensures modularity, scalability, and efficient inter-process communication. Signal correction algorithms, such as Asymmetric Least Squares (ALS) baseline smoothing and a numerical droop correction approach, were implemented and tested using Linac beam data. The results confirm that this system improves signal fidelity and supports real-time analysis requirements, offering valuable contributions to the field of beam instrumentation and data acquisition systems.

Bowers, Elliot [Cabrillo Coll.]↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

Bowers, Elliot [Cabrillo Coll.; Fermilab]↗

NASA NTRS1988

Functional description of signal processing in the Rogue GPS receiver

Over the past year, two Rogue GPS prototype receivers have been assembled and successfully subjected to a variety of laboratory and field tests. A functional description is presented of signal processing in the Rogue receiver, tracing the signal from RF input to the output values of group delay, phase, and data bits. The receiver can track up to eight satellites, without time multiplexing among satellites or channels, simultaneously measuring both group delay and phase for each of three channels (L1-C/A, L1-P, L2-P). The Rogue signal processing described requires generation of the code for all three channels. Receiver functional design, which emphasized accuracy, reliability, flexibility, and dynamic capability, is summarized. A detailed functional description of signal processing is presented, including C/A-channel and P-channel processing, carrier-aided averaging of group delays, checks for cycle slips, acquistion, and distinctive features.

Thomas, J. B.↗