Compact Receivers and Smart-Pixel Chips for Optical Interconnects and Signal Processing
A continuous-time shift-invariant cellular neural network (CNN) with hardware annealing
Smart↗
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Results for “SIGNAL PROCESSING”
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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Radar Ambiguities and Signal Processing Design Tradeoffs in the REASON Radar Sounder
NASA’s planned Europa Clipper mission would carry a radar sounder which operates at HF and VHF frequencies that can penetrate the icy shell of Europa. The sounder would be used to characterize subsurface bodies of liquid water and to measure the thickness of the icy shell. The spacecraft would make observations during flyby to reduce radiation exposure. The varying geometry during the flyby is expected to cause varying radar ambiguity and surface clutter contamination. At the same time, data down-link restrictions would require on-board pulse summation to reduce data volume which would require on-board Doppler filtering to control clutter and ambiguity contamination levels. This paper will study some of the design trade-offs involved in specifying the on-board Doppler filtering.
Hawkins, D.↗
Stochastic Signal Processing and Machine Learning Approaches for Exoplanet Transit Surveys
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TESS↗
Signal Processing of Images for Convective Boundary Layer Height Estimation From Radar (SPICER) and Multi-Instrument Verification
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54 ENVIRONMENTAL SCIENCES↗
Signal Processing of Images for Convective Boundary Layer Height Estimation From Radar (SPICER) and Multi-Instrument Verification
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54 ENVIRONMENTAL SCIENCES↗
Machine Learning for Automated Weld Quality Monitoring and Control
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
99 GENERAL AND MISCELLANEOUS↗
Machine Learning for Automated Weld Quality Monitoring and Control
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
42 ENGINEERING↗
Parallel algorithms for mapping pipelined and parallel computations
Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.
Nicol, David M.↗
Performance of Residual Carrier Array Feed Combining in Correlated Noise
An array feed combining system for the recovery of SNR loss due to antenna reflector deformation has been implemented and is currently being evaluated on the Jet Propulsion Laboratory 34 meter DSS-13 antenna. In this system, the defocused signal field captured by a focal plane array feed is recovered using real-time signal processing and signal combining techniques. The current signal processing and signal combining algorithms are optimum under the assumption that the white Gaussian noise processes in the received signals from different array elements are mutually uncorrelated. Experimental data at DSS-13 indicate that these noise processes are indeed mutually correlated. The main result of this paper is an analytical derivation of the actual SNR performance of the current suboptimal signal combining algorithm in this correlated noises environment. The analysis here shows that the combined signal SNR can either be improved or degraded depending on the relation between the array signal and noise correlation coefficient phases. Further performance improvement will require the development of signal combining methods that take into account the correlated noises.
Residual↗
Performance of residual carrier array-feed combining in correlated noise
An array feed combining system for the recovery of signal-to-noise ratio (SNR) loss due to antenna reflector deformation has been implemented and is currently being evaluated on the Jet Propulsion Laboratory's 34-meter DSS-13 antenna. In this system, the defocused signal field captured by a focal plane array feed is recovered using real-time signal-processing and signal-combining techniques. The current signal-processing and signal-combining algorithms are optimum under the assumption that the white Gaussian noise processes in the received signals from different array elements are mutually uncorrelated. Experimental data at DSS 13 indicate that these noise processes are indeed mutually correlated. The main result of this article is an analytical derivation of the actual SNR performance of the current suboptimal signal-combining algorithm in this correlated-noise environment. The analysis here shows that the combined signal SNR can either be improved or degraded depending on the relation between the array signal and noise correlation coefficient phases. Further performance improvement will require the development of signal-combining methods that take into account the correlated noises.
Tan, H. H.↗
Advanced multispectral remote sensing technology and applications; Proceedings of the Conference, Arlington, VA, May 6, 7, 1982
Sensor system concepts are discussed, taking into account a multispectral linear array instrument design, an improved earth resources sensing instrument, design tradeoffs for a multispectral linear array instrument, imaging spectrometer technologies for advanced earth remote sensing, and the design concept for an optimized earth radiation budget sensor. Topics related to supporting optics and signal processing methodologies are explored, giving attention to two mirror objective design for multispectral remote sensing, the measurement of optical modulation functions in sparsely sampled mosaic focal plane arrays, multispectral linear array focal plane signal processing, signal processing for the NASA multispectral linear array, the concept for a multiple resolution pushbroom sensor, and the evolution of the octal radiometer. Techniques and applications for multispectral data are considered along with aspects of focal plane technology.
Ando, K. J.↗
Low Temperature Performance of High-Speed Neural Network Circuits
Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.
artificial neural networks image processing signal↗
Dimensionally Aligned Signal Projection Algorithms Library
Dimensionally aligned signal projection (DASP) algorithms are used to analyze fast Fourier transforms (FFTs) and generate visualizations that help focus on the harmonics for specific signals. At a high level, these algorithms extract the FFT segments around each harmonic frequency center, and then align them in equally sized arrays ordered by increasing distance from the base frequency. This allows for a focused view of the harmonic frequencies, which, among other use cases, can enable machine learning algorithms to more easily identify salient patterns. This work seeks to provide an effective open-source implementation of the DASP algorithms proposed by Vann et al. (2018) as well as functionality to help explore and test how these algorithms work with an interactive dashboard and signal-generation tool. The DASP library is implemented in Python and contains four types of algorithms for implementing these feature engineering techniques: fixed harmonically aligned signal projection (HASP), decimating HASP, interpolating HASP, and frequency aligned signal projection (FASP). Each algorithm returns a numerical array, which can be visualized as an image. The HASP algorithms are variations of the algorithms originally presented by Vann et al. (2018). For consistency, FASP, which is the terminology used for the short-time Fourier transform (STFT), has been implemented as part of the library to provide a similar interface to the STFT of the raw signal. Additionally, the library contains an algorithm to generate artificial signals with basic customizations such as the base frequency, sample rate, duration, number of harmonics, noise, and number of signals. Finally, the library provides multiple interactive visualizations, each of which is implemented using IPyWidgets and works in a Jupyter environment. A dashboard-style visualization is provided, which contains some common signal-processing visual components (signal, FFT, spectogram) updating in unison with the HASP functions (see Figure 1 below). Separate from the dashboard, an independent visualization is provided for each of the DASP algorithms as well as for the artifical signal generator. These visualizations are included in the library to aid in developing an intuitive understanding how the algorithms are affected by different input signals and parameter selections.
harmonics↗
Benchmarking and Testing of Qualcomm Snapdragon System-on-Chip for JPL Space Applications and Missions
As some space missions become more challenging due to new environments, greater distances, or more limited size, weight, and power (SWaP) constraints, spacecraft avionics must adapt to allow the spacecraft to be more autonomous and agile---eliminating the Spacecraft-Earth-Spacecraft feedback loop whenever possible. Prime examples of such missions include Aerobots (such as Ingenuity with extremely low SWaP constraints and demanding signal/image processing during flight) and landers in possibly hostile environments (such as a Europa lander mission, with limited communication capacity, high latency, and constrained power budget). To address these challenges, JPL worked with Qualcomm to demonstrate the use of their Snapdragon 801 system-on-chip (SoC) onboard the Ingenuity Helicopter on Mars. The Qualcomm Snapdragon SoC contains various subsystems, including an ARM cluster, a Graphics processing unit, a Digital Signal Processing subsystem, a Neural Processing Engine, Image Signal Processing subsystem, among others. Since the success of Ingenuity, JPL is continuing to work with Qualcomm to address other applications of the Snapdragon SoC technology. This includes the deployment of two 855 Snapdragon development boards onboard the International Space Station (ISS) for successful in-situ benchmarking of applications in space (beyond those tested on Ingenuity). In this paper, we will examine the performance of various applications that have been identified to benefit from greater onboard computational capability. These applications include (among others): machine vision algorithms that are expected to be critical in autonomous entry-descent-and-landing scenarios and real-time Aerobot flight navigation; Hyperspectral compression algorithms; Synthetic Aperture Radar Processing along with various instrument processing algorithms. We discuss how the infusion of Qualcomm's Snapdragon SoC is capable of enabling missions that may not have been able to achieve their goals with traditional flight computing. In addition, we also show that for some algorithms, the software implementation on the Snapdragon SoC outperforms traditional FPGA implementations.
Cretu, Vlad↗
Structural insights into strigolactone catabolism by carboxylesterases reveal a conserved conformational regulation
Phytohormone levels are regulated through specialized enzymes, participating not only in their biosynthesis but also in post-signaling processes for signal inactivation and cue depletion. Arabidopsis thaliana (At) carboxylesterase 15 (CXE15) and carboxylesterase 20 (CXE20) have been shown to deplete strigolactones (SLs) that coordinate various growth and developmental processes and function as signaling molecules in the rhizosphere. Here, we elucidate the X-ray crystal structures of AtCXE15 (both apo and SL intermediate bound) and AtCXE20, revealing insights into the mechanisms of SL binding and catabolism. The N-terminal regions of CXE15 and CXE20 exhibit distinct secondary structures, with CXE15 characterized by an alpha helix and CXE20 by an alpha/beta fold. These structural differences play pivotal roles in regulating variable SL hydrolysis rates. Our findings, both in vitro and in planta, indicate that a transition of the N-terminal helix domain of CXE15 between open and closed forms facilitates robust SL hydrolysis. The results not only illuminate the distinctive process of phytohormone breakdown but also uncover a molecular architecture and mode of plasticity within a specific class of carboxylesterases.
59 BASIC BIOLOGICAL SCIENCES↗
Eliminating "Hotspots" in Digital Image Processing
Signals from defective picture elements rejected. Image processing program for use with charge-coupled device (CCD) or other mosaic imager augmented with algorithm that compensates for common type of electronic defect. Algorithm prevents false interpretation of "hotspots". Used for robotics, image enhancement, image analysis and digital television.
Salomon, P. M.↗
Assessment and Usage of In-Situ Monitoring Data for American Society of Mechanical Engineers Part Qualification
In-situ monitoring and anomaly detection are important components for qualification of directed energy deposition (DED) additive manufacturing (AM) processes and components. The use of in-situ monitoring requires an understanding of anomalies that can be identified during the process and how those anomalies correlate to mechanical properties of the component post-production. There is also a need to qualify the algorithms and software used to interpret the process signals for DED AM. There is no single process signal that can be used with a single algorithm that will identify all anomalies that will translate to a defect in a process. The process signals are affected by changes in material, location, resolution, acquisition rate, component geometry, and the machine itself. It is observed that multiple process signals are required to identify relevant features that can be correlated to mechanical properties.
36 MATERIALS SCIENCE↗
Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning
Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.
42 ENGINEERING↗