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NASA NTRS2016

An Objective Classification of Saturn Cloud Features from Cassini ISS Images

A k -means clustering algorithm is applied to Cassini Imaging Science Subsystem continuum and methane band images of Saturn's northern hemisphere to objectively classify regional albedo features and aid in their dynamical interpretation. The procedure is based on a technique applied previously to visible- infrared images of Earth. It provides a new perspective on giant planet cloud morphology and its relationship to the dynamics and a meteorological context for the analysis of other types of simultaneous Saturn observations. The method identifies 6 clusters that exhibit distinct morphology, vertical structure, and preferred latitudes of occurrence. These correspond to areas dominated by deep convective cells; low contrast areas, some including thinner and thicker clouds possibly associated with baroclinic instability; regions with possible isolated thin cirrus clouds; darker areas due to thinner low level clouds or clearer skies due to downwelling, or due to absorbing particles; and fields of relatively shallow cumulus clouds. The spatial associations among these cloud types suggest that dynamically, there are three distinct types of latitude bands on Saturn: deep convectively disturbed latitudes in cyclonic shear regions poleward of the eastward jets; convectively suppressed regions near and surrounding the westward jets; and baro-clinically unstable latitudes near eastward jet cores and in the anti-cyclonic regions equatorward of them. These are roughly analogous to some of the features of Earth's tropics, subtropics, and midlatitudes, respectively. This classification may be more useful for dynamics purposes than the traditional belt-zone partitioning. Temporal variations of feature contrast and cluster occurrence suggest that the upper tropospheric haze in the northern hemisphere may have thickened by 2014. The results suggest that routine use of clustering may be a worthwhile complement to many different types of planetary atmospheric data analysis.

atmosphere dynamics↗

DOE OSTI2021

Photoacoustic 3-D imaging of polycrystalline microstructure improved with transverse acoustic waves

Non-invasive fast imaging of grain microstructure of polycrystalline ceria with sub-micrometric spatial resolution is performed via time-domain Brillouin scattering. The propagation of a nanoacoustic pulse is monitored down to 8 µm deep in a 30 × 30 µm2 area. Grains boundaries are reconstructed in three-dimensions via a twostep processing method, relying on the wavelet synchro-squeezed transform and the alpha shape algorithm. Imaging contrast is improved by taking advantage of stronger sensitivity to anisotropy of transverse acoustic waves, compared with longitudinal waves. Utilization of transverse waves in the image processing reveals additional boundaries that are not discerned using longitudinal waves, confirmed by an electron backscattering diffraction pattern. A buried inclined interface between differently oriented grains is identified by monitoring changes in amplitude (phase) of the portion of the signal associated with transverse (longitudinal) waves. Estimates of the inclination angle of this interface prove the sensitivity of our laser ultrasonic method to image inclined boundaries.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗

NASA NTRS1985

Advanced optical position sensors for magnetically suspended wind tunnel models

A major concern to aerodynamicists has been the corruption of wind tunnel test data by model support structures, such as stings or struts. A technique for magnetically suspending wind tunnel models was considered by Tournier and Laurenceau (1957) in order to overcome this problem. This technique is now implemented with the aid of a Large Magnetic Suspension and Balance System (LMSBS) and advanced position sensors for measuring model attitude and position within the test section. Two different optical position sensors are discussed, taking into account a device based on the use of linear CCD arrays, and a device utilizing area CID cameras. Current techniques in image processing have been employed to develop target tracking algorithms capable of subpixel resolution for the sensors. The algorithms are discussed in detail, and some preliminary test results are reported.

Lafleur, S.↗

NASA NTRS1988

Knowledge-based low-level image analysis for computer vision systems

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Dhawan, Atam P.↗

NASA NTRS1990

A positional estimation technique for an autonomous land vehicle in an unstructured environment

This paper presents a solution to the positional estimation problem of an autonomous land vehicle navigating in an unstructured mountainous terrain. A Digital Elevation Map (DEM) of the area in which the robot is to navigate is assumed to be given. It is also assumed that the robot is equipped with a camera that can be panned and tilted, and a device to measure the elevation of the robot above the ground surface. No recognizable landmarks are assumed to be present in the environment in which the robot is to navigate. The solution presented makes use of the DEM information, and structures the problem as a heuristic search in the DEM for the possible robot location. The shape and position of the horizon line in the image plane and the known camera geometry of the perspective projection are used as parameters to search the DEM. Various heuristics drawn from the geometric constraints are used to prune the search space significantly. The algorithm is made robust to errors in the imaging process by accounting for the worst care errors. The approach is tested using DEM data of areas in Colorado and Texas. The method is suitable for use in outdoor mobile robots and planetary rovers.

Talluri, Raj↗

NASA NTRS2000

Overcoming Dynamic Disturbances in Imaging Systems

We develop and discuss a methodology with the potential to yield a significant reduction in complexity, cost, and risk of space-borne optical systems in the presence of dynamic disturbances. More robust systems almost certainly will be a result as well. Many future space-based and ground-based optical systems will employ optical control systems to enhance imaging performance. The goal of the optical control subsystem is to determine the wavefront aberrations and remove them. Ideally reducing an aberrated image of the object under investigation to a sufficiently clear (usually diffraction-limited) image. Control will likely be distributed over several elements. These elements may include telescope primary segments, telescope secondary, telescope tertiary, deformable mirror(s), fine steering mirror(s), etc. The last two elements, in particular, may have to provide dynamic control. These control subsystems may become elaborate indeed. But robust system performance will require evaluation of the image quality over a substantial range and in a dynamic environment. Candidate systems for improvement in the Earth Sciences Enterprise could include next generation Landsat systems or atmospheric sensors for dynamic imaging of individual, severe storms. The technology developed here could have a substantial impact on the development of new systems in the Space Science Enterprise; such as the Next Generation Space Telescope(NGST) and its follow-on the Next NGST. Large Interferometric Systems of non-zero field, such as Planet Finder and Submillimeter Probe of the Evolution of Cosmic Structure, could benefit. These systems most likely will contain large, flexible optomechanical structures subject to dynamic disturbance. Furthermore, large systems for high resolution imaging of planets or the sun from space may also benefit. Tactical and Strategic Defense systems will need to image very small targets as well and could benefit from the technology developed here. We discuss a novel speckle imaging technique with the potential to separate dynamic aberrations from static aberrations. Post-processing of a set of image data, using an algorithm based on this technique, should work for all but the lowest light levels and highest frequency dynamic environments. This technique may serve to reduce the complexity of the control system and provide for robust, fault-tolerant, reduced risk operation. For a given object, a short exposure image is "frozen" on the focal plane in the presence of the environmental disturbance (turbulence, jitter, etc.). A key factor is that this imaging data exhibits frame-to-frame linear shift invariance. Therefore, although the Point Spread Function is varying from frame to frame, the source is fixed; and each short exposure contains object spectrum data out to the diffraction limit of the imaging system. This novel speckle imaging technique uses the Knox-Thompson method. The magnitude of the complex object spectrum is straightforward to determine by well-established approaches. The phase of the complex object spectrum is decomposed into two parts. One is a single-valued function determined by the divergence of the optical phase gradient. The other is a multi-valued function determined by the circulation of the optical phase gradient-"hidden phase." Finite difference equations are developed for the phase. The novelty of this approach is captured in the inclusion of this "hidden phase." This technique allows the diffraction-limited reconstruction of the object from the ensemble of short exposure frames while simultaneously estimating the phase as a function of time from a set of exposures.

Young, Eric W.↗

NASA NTRS2000

Overcoming Dynamic Disturbances in Imaging Systems

We develop and discuss a methodology with the potential to yield a significant reduction in complexity, cost, and risk of space-borne optical systems in the presence of dynamic disturbances. More robust systems almost certainly will be a result as well. Many future space-based and ground-based optical systems will employ optical control systems to enhance imaging performance. The goal of the optical control subsystem is to determine the wavefront aberrations and remove them. Ideally reducing an aberrated image of the object under investigation to a sufficiently clear (usually diffraction-limited) image. Control will likely be distributed over several elements. These elements may include telescope primary segments, telescope secondary, telescope tertiary, deformable mirror(s), fine steering mirror(s), etc. The last two elements, in particular, may have to provide dynamic control. These control subsystems may become elaborate indeed. But robust system performance will require evaluation of the image quality over a substantial range and in a dynamic environment. Candidate systems for improvement in the Earth Sciences Enterprise could include next generation Landsat systems or atmospheric sensors for dynamic imaging of individual, severe storms. The technology developed here could have a substantial impact on the development of new systems in the Space Science Enterprise; such as the Next Generation Space Telescope(NGST) and its follow-on the Next NGST. Large Interferometric Systems of non-zero field, such as Planet Finder and Submillimeter Probe of the Evolution of Cosmic Structure, could benefit. These systems most likely will contain large, flexible optormechanical structures subject to dynamic disturbance. Furthermore, large systems for high resolution imaging of planets or the sun from space may also benefit. Tactical and Strategic Defense systems will need to image very small targets as well and could benefit from the technology developed here. We discuss a novel speckle imaging technique with the potential to separate dynamic aberrations from static aberrations. Post-processing of a set of image data, using an algorithm based on this technique, should work for all but the lowest light levels and highest frequency dynamic environments. This technique may serve to reduce the complexity of the control system and provide for robust, fault-tolerant, reduced risk operation. For a given object, a short exposure image is "frozen" on the focal plane in the presence of the environmental disturbance (turbulence, jitter, etc.). A key factor is that this imaging data exhibits frame-to-frame linear shift invariance. Therefore, although the Point Spread Function is varying from frame to frame, the source is fixed; and each short exposure contains object spectrum data out to the diffraction limit of the imaging system. This novel speckle imaging technique uses the Knox-Thompson method. The magnitude of the complex object spectrum is straightforward to determine by well-established approaches. The phase of the complex object spectrum is decomposed into two parts. One is a single-valued function determined by the divergence of the optical phase gradient. The other is a multi-valued function determined by, the circulation of the optical phase gradient-"hidden phase." Finite difference equations are developed for the phase. The novelty of this approach is captured in the inclusion of this "hidden phase." This technique allows the diffraction-limited reconstruction of the object from the ensemble of short exposure frames while simultaneously estimating the phase as a function of time from a set of exposures.

Young, Eric W.↗

NASA NTRS1992

Description of a prototype emission-transmission computed tomography imaging system

We have developed a prototype imaging system that can perform simultaneous x-ray transmission CT and SPECT phantom studies. This system employs a 23-element high-purity-germanium detector array. The detector array is coupled to a collimator with septa angled toward the focal spot of an x-ray tube. During image acquisition, the x-ray fan beam and the detector array move synchronously along an arc pivoted at the x-ray source. Multiple projections are obtained by rotating the object, which is mounted at the center of rotation of the system. The detector array and electronics can count up to 10(6) cps/element with sufficient energy-resolution to discriminate between x-rays at 100-120 kVp and gamma rays from 99mTc. We have used this device to acquire x-ray CT and SPECT images of a three-dimensional Hoffman brain phantom. The emission and transmission images may be superimposed in order to localize the emission image on the transmission map.

NASA Discipline Musculoskeletal↗

NASA NTRS2007

Fusing Image Data for Calculating Position of an Object

A computer program has been written for use in maintaining the calibration, with respect to the positions of imaged objects, of a stereoscopic pair of cameras on each of the Mars Explorer Rovers Spirit and Opportunity. The program identifies and locates a known object in the images. The object in question is part of a Moessbauer spectrometer located at the tip of a robot arm, the kinematics of which are known. In the program, the images are processed through a module that extracts edges, combines the edges into line segments, and then derives ellipse centroids from the line segments. The images are also processed by a feature-extraction algorithm that performs a wavelet analysis, then performs a pattern-recognition operation in the wavelet-coefficient space to determine matches to a texture feature measure derived from the horizontal, vertical, and diagonal coefficients. The centroids from the ellipse finder and the wavelet feature matcher are then fused to determine co-location. In the event that a match is found, the centroid (or centroids if multiple matches are present) is reported. If no match is found, the process reports the results of the analyses for further examination by human experts.

Huntsberger, Terrance↗

NASA NTRS2018

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing:Pattern Recognition↗

NASA NTRS1988

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.↗

NASA NTRS2017

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Image Processing; Pattern Recognition↗

NASA NTRS2005

Methodology for the Elimination of Reflection and System Vibration Effects in Particle Image Velocimetry Data Processing

A methodology to eliminate model reflection and system vibration effects from post processed particle image velocimetry data is presented. Reflection and vibration lead to loss of data, and biased velocity calculations in PIV processing. A series of algorithms were developed to alleviate these problems. Reflections emanating from the model surface caused by the laser light sheet are removed from the PIV images by subtracting an image in which only the reflections are visible from all of the images within a data acquisition set. The result is a set of PIV images where only the seeded particles are apparent. Fiduciary marks painted on the surface of the test model were used as reference points in the images. By locating the centroids of these marks it was possible to shift all of the images to a common reference frame. This image alignment procedure as well as the subtraction of model reflection are performed in a first algorithm. Once the images have been shifted, they are compared with a background image that was recorded under no flow conditions. The second and third algorithms find the coordinates of fiduciary marks in the acquisition set images and the background image and calculate the displacement between these images. The final algorithm shifts all of the images so that fiduciary mark centroids lie in the same location as the background image centroids. This methodology effectively eliminated the effects of vibration so that unbiased data could be used for PIV processing. The PIV data used for this work was generated at the NASA Langley Research Center Quiet Flow Facility. The experiment entailed flow visualization near the flap side edge region of an airfoil model. Commercial PIV software was used for data acquisition and processing. In this paper, the experiment and the PIV acquisition of the data are described. The methodology used to develop the algorithms for reflection and system vibration removal is stated, and the implementation, testing and validation of these algorithms are presented.

Bremmer, David M.↗

NASA NTRS1990

Hybrid image processing

Partly-digital, partly-optical 'hybrid' image processing attempts to use the properties of each domain to synergistic advantage: while Fourier optics furnishes speed, digital processing allows the use of much greater algorithmic complexity. The video-rate image-coordinate transformation used is a critical technology for real-time hybrid image-pattern recognition. Attention is given to the separation of pose variables, image registration, and both single- and multiple-frame registration.

Juday, Richard D.↗

NASA NTRS2010

Platform for Post-Processing Waveform-Based NDE

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Roth, Don J.↗