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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Coherent Detection of High-Rate Optical PPM Signals

A method of coherent detection of high-rate pulse-position modulation (PPM) on a received laser beam has been conceived as a means of reducing the deleterious effects of noise and atmospheric turbulence in free-space optical communication using focal-plane detector array technologies. In comparison with a receiver based on direct detection of the intensity modulation of a PPM signal, a receiver based on the present method of coherent detection performs well at much higher background levels. In principle, the coherent-detection receiver can exhibit quantum-limited performance despite atmospheric turbulence. The key components of such a receiver include standard receiver optics, a laser that serves as a local oscillator, a focal-plane array of photodetectors, and a signal-processing and data-acquisition assembly needed to sample the focal-plane fields and reconstruct the pulsed signal prior to detection. The received PPM-modulated laser beam and the local-oscillator beam are focused onto the photodetector array, where they are mixed in the detection process. The two lasers are of the same or nearly the same frequency. If the two lasers are of different frequencies, then the coherent detection process is characterized as heterodyne and, using traditional heterodyne-detection terminology, the difference between the two laser frequencies is denoted the intermediate frequency (IF). If the two laser beams are of the same frequency and remain aligned in phase, then the coherent detection process is characterized as homodyne (essentially, heterodyne detection at zero IF). As a result of the inherent squaring operation of each photodetector, the output current includes an IF component that contains the signal modulation. The amplitude of the IF component is proportional to the product of the local-oscillator signal amplitude and the PPM signal amplitude. Hence, by using a sufficiently strong local-oscillator signal, one can make the PPM-modulated IF signal strong enough to overcome thermal noise in the receiver circuits: this is what makes it possible to achieve near-quantum-limited detection in the presence of strong background. Following quantum-limited coherent detection, the outputs of the individual photodetectors are automatically aligned in phase by use of one or more adaptive array compensation algorithms [e.g., the least-mean-square (LMS) algorithm]. Then the outputs are combined and the resulting signal is processed to extract the high-rate information, as though the PPM signal were received by a single photodetector. In a continuing series of experiments to test this method (see Fig. 1), the local oscillator has a wavelength of 1,064 nm, and another laser is used as a signal transmitter at a slightly different wavelength to establish an IF of about 6 MHz. There are 16 photodetectors in a 4 4 focal-plane array; the detector outputs are digitized at a sampling rate of 25 MHz, and the signals in digital form are combined by use of the LMS algorithm. Convergence of the adaptive combining algorithm in the presence of simulated atmospheric turbulence for optical PPM signals has already been demonstrated in the laboratory; the combined output is shown in Fig. 2(a), and Fig. 2(b) shows the behavior of the phase of the combining weights as a function of time (or samples). We observe that the phase of the weights has a sawtooth shape due to the continuously changing phase in the down-converted output, which is not exactly at zero frequency. Detailed performance analysis of this coherent free-space optical communication system in the presence of simulated atmospheric turbulence is currently under way.

Vilnrotter, Victor↗
DOE OSTI2015

Detection of Periodic Forced Oscillations in Power Systems

An algorithm for the detection and frequency estimation of forced oscillations in power systems is proposed. The method operates by comparing the periodogram of synchrophasor measurements to a detection threshold. This threshold is established by deriving a general expression for the distribution of the periodogram and is related to the algorithm's probabilities of false alarm and detection. Unlike classic detection algorithms designed for use with white Gaussian noise, the proposed algorithm uses a detection threshold that varies with frequency to account for the colored nature of synchrophasor measurements. Further, a detection scheme based on multiple segments of data is proposed to improve the algorithm's performance as a monitoring tool in the online environment. A design approach that helps to ensure that the best-available probability of detection from any one detection segment is constantly increasing with the duration of the forced oscillation is also developed. Results from application of the detection algorithm to simulated and measured power system data suggest that the algorithm provides the expected detection performance and can be used to detect forced oscillations in practical monitoring of power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 $M_w$ 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. In this work, our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

58 GEOSCIENCES↗
DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Evaluation of Precipitation Detection over Various Surfaces from Passive Microwave Imagers and Sounders

During the middle part of this decade a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3 hr), global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation. The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation, are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22 mm/hr minimum threshold and 90% of precipitation detected) and AMSU (0.26 mm/hr minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74 mm/hr threshold and 12% detected) and snow-covered surfaces (0.44 mm/hr threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a-priori atmospheric state and all instruments benefit from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms.

GPM↗
NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark‐Target Algorithm: 1. Dust Detection

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark-Target (DT) algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near-UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET-MODIS data set and collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-MODIS data set. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1–2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with aerosol optical depth (AOD) and Angstrom exponent (AE), the new dust detection algorithm finds more dusty pixels and fewer false detections.

spectral dust detection↗
DOE OSTI2014

Effective detection of CO 2 leakage: a comparison of groundwater sampling and pressure monitoring

Shallow aquifer monitoring is likely to be a required aspect to any geologic CO 2 sequestration operation. Collecting groundwater samples and analyzing for geochemical parameters such as pH, alkalinity, total dissolved carbon, and trace metals has been suggested by a number of authors as a possible strategy to detect CO 2 leakage. The effectiveness of this approach, however, will depend on the hydrodynamics of the leak-induced CO 2 plume and the spatial distribution of the monitoring wells relative to the origin of the leak. To our knowledge, the expected effectiveness of groundwater sampling to detect CO 2 leakage has not yet been quantitatively assessed. In this study we query hundreds of simulations developed for the National Risk Assessment Project (US DOE) to estimate risks to drinking water resources associated with CO 2 leaks. The ensemble of simulations represent transient, 3-D multi-phase reactive transport of CO 2 and brine leaked from a sequestration reservoir, via a leaky wellbore, into an unconfined aquifer. Key characteristics of the aquifer, including thickness, mean permeability, background hydraulic gradient, and geostatistical measures of aquifer heterogeneity, were all considered uncertain parameters. Complex temporally-varying CO 2 and brine leak rate scenarios were simulated using a heuristic scheme with ten uncertain parameters. The simulations collectively predict the spatial and temporal evolution of CO 2 and brine plumes over 200 years in a shallow aquifer under a wide range of leakage scenarios and aquifer characteristics. Using spatial data from an existing network of shallow drinking water wells in the Edwards Aquifer, TX, as one illustrative example, we calculated the likelihood of leakage detection by groundwater sampling. In this monitoring example, there are 128 wells available for sampling, with a density of about 2.6 wells per square kilometer. If the location of the leak is unknown a priori, a reasonable assumption in many cases, we found that the leak would be detected in at least one of the monitoring wells in less than 10% of the scenarios considered. This is because plume sizes are relatively small, and so the probability of detection decreases rapidly with distance from the leakage point. For example, 400m away from the leakage point there is less than 20% chance of detection. We then compared the effectiveness of groundwater quality sampling to shallow aquifer and/or reservoir pressure monitoring. For the Edwards Aquifer example, pressure monitoring in the same monitoring well network was found to be even less effective that groundwater quality monitoring. This is presumably due to the unconfined conditions and relatively high permeability, so pressure perturbations quickly dissipate. Although specific results may differ from site to site, this type of analysis should be useful to site operators and regulators when selecting leak detection strategies. Given the spatial characteristics of a proposed monitoring well network, probabilities of leakage detection can be rapidly calculated using this methodology. Although conditions such as these may not be favorable for leakage detection in shallow aquifers, leakage detection could be much more successful in the injection reservoir. We demonstrate proof-of-concept for this hypothesis, presenting a simulation where there is measurable pressure change at the injection well due to overpressurization, fault rupture, and consequent leakage up the fault into intermediate and shallow aquifers. The size of the detectible pressure change footprint is much larger in the reservoir than in either of the overlying aquifers. Further exploration of the range of conditions for which this technique would be successful is the topic of current study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Aerosol detection efficiency in inductively coupled plasma mass spectrometry

We used an electrostatic size classification technique to segregate particles of known composition prior to being injected into an inductively coupled plasma mass spectrometer (ICP-MS). Moreover, we counted size-segregated particles with a condensation nuclei counter as well as sampled with an ICP-MS. By injecting particles of known size, composition, and aerosol concentration into the ICP-MS, efficiencies of the order of magnitude aerosol detection were calculated, and the particle size dependencies for volatile and refractory species were quantified. Similar to laser ablation ICP-MS, aerosol detection efficiency was defined as the rate at which atoms were detected in the ICP-MS normalized by the rate at which atoms were injected in the form of particles. This method adds valuable insight into the development of technologies like laser ablation ICP-MS where aerosol particles (of relatively unknown size and gas concentration) are generated during ablation and then transported into the plasma of an ICP-MS. In this study, we characterized aerosol detection efficiencies of volatile species gold and silver along with refractory species aluminum oxide, cerium oxide, and yttrium oxide. Aerosols were generated with electrical mobility diameters ranging from 100 to 1000 nm. In general, it was observed that refractory species had lower aerosol detection efficiencies than volatile species, and there were strong dependencies on particle size and plasma torch residence time. Volatile species showed a distinct transition point at which aerosol detection efficiency began decreasing with increasing particle size. This critical diameter indicated the largest particle size for which complete particle detection should be expected and agreed with theories published in other works. Aerosol detection efficiencies also displayed power law dependencies on particle size. Aerosol detection efficiencies ranged from 10 -5 to 10 -11 . Free molecular heat and mass transfer theory was applied, but evaporative phenomena were not sufficient to explain the dependence of aerosol detection on particle diameter. Additional work is needed to correlate experimental data with theory for metal-oxides where thermodynamic property data are sparse relative to pure elements. Finally, when matrix effects and the diffusion of ions inside the plasma were considered, mass loading was concluded to have had an effect on the dependence of detection efficiency on particle diameter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Change Detection in Constellations of Buried Objects Extracted From Ground-Penetrating Radar Data

Detection of deliberately buried objects in ground-penetrating radar (GPR) data acquired along a path is a clutter-limited problem. Detection-false alarm rate performance can be improved by replacing the detection statistic with a change statistic that incorporates information from previous path traversals. A constellation matching approach is developed for buried-object change detection in GPR data. Network topologies of buried objects detected in GPR data from previous path traversals are maintained in a constellation database. Localized groups of buried objects newly detected on the latest path traversal are matched to the constellation. Buried objects from the latest path traversal whose locations or strengths cannot be reconciled with the constellation are identified as changes. The system has one component that generates constellation databases offline and another component suitable for change detection in real time. It can tolerate paths with significant translational misalignments. The system uses the following: 1) a customized translational relaxation algorithm for point pattern matching that incorporates detection strength and a probabilistic uncertainty model for buried-object location into the objective function and 2) a change statistic that accounts for the magnitude of change relative to predicted detection strength. A constellation database can typically be generated offline from a single path traversal roughly two orders of magnitude faster than the time typically required for a vehicle to travel the extent of the path. Database sizes are typically four to five orders of magnitude smaller than the data sets of GPR signal scans or focused 3-D GPR images that they were generated from. On bumpy dirt roads buried exclusively with nonmetallic objects at various depths, detection-false alarm rate performance is shown to be significantly better for our change statistics than for our detection statistics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross-correlation analysis

Purpose: To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Methods: Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragments within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method’s performance. Results: The method detected calcium fragments with sizes of 0.14–0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4–1.0 mm in images with voxel sizes between (0.2 mm){sup 3} and (0.6 mm){sup 3}. In images acquired at 7 T with voxel sizes of (0.2 mm){sup 3}–(0.4 mm){sup 3}, calcium fragments (size 0.3–0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%–90%, 51%–68%, and 0.77%–0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times below 12 min, and voxel sizes of (0.4 mm){sup 3}–(0.6 mm){sup 3}, simulated microcalcifications with sizes of 0.6–1.0 mm were detected with a sensitivity, specificity, and AUC of 75%–87%, 54%–87%, and 0.76%–0.90%, respectively. However, different microcalcification shapes were indistinguishable. Conclusions: The new method is promising for detecting relatively large microcalcifications (i.e., 0.6–0.9 mm) within the breast at 7 T in reasonable times. Detection of smaller deposits at high field may be possible with higher spatial resolution, but such images require relatively long scan times. Although mammography can detect and distinguish the shape of smaller microcalcifications with superior sensitivity and specificity, this alternative method does not expose tissue to ionizing radiation, is not affected by breast density, and can be combined with other MRI methods (e.g., dynamic contrast-enhanced MRI and diffusion weighted MRI), to potentially improve diagnostic performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically during the documentation of patient positioning and localization of the patient. Incidents were most frequently detected during treatment delivery (30%), and incidents identified at this point also had higher severity scores than other workflow areas (NMRI = 1.6). Incidents identified during on-treatment quality management were also more severe (NMRI = 1.7), and the specific process steps of reviewing portal and CBCT images tended to catch highest-severity incidents. On average, safety barriers caught 46% of all incidents, most frequently at physics chart review, therapist’s chart check, and the review of portal images; however, most of the incidents that pass through a particular safety barrier are not designed to be capable of being captured at that barrier. Conclusions: Incident learning systems can be used to assess the most common points of error origination and detection in radiation oncology. This can help tailor safety improvement efforts and target the highest impact portions of the workflow. The most severe near-miss events tend to originate during simulation, with the most severe near-miss events detected at the time of patient treatment. Safety barriers can be improved to allow earlier detection of near-miss events.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-207A-09: Tailoring TCM Schemes to a Task: Evaluating the Impact of Customized TCM Profiles On Detection of Lung Nodules in Simulated CT Lung Cancer Screening

Purpose: Recent work has shown that current TCM profile designs boost detection of low-contrast lung lesions in the lung apices, but yield reduced detection performance in the mid and lower lung regions relative to fixed tube current cases. This observed imbalance suggests that the TCM scheme might be tailored in new ways to maximize nodule detection throughout the entire lung. In this work, we begin a preliminary investigation into custom TCM profiles in an attempt to achieve uniform lesion detection throughout the extent of the lung. Methods: Low-contrast (25HU), 6mm nodules representing ground glass opacities were simulated at 1mm intervals over the length the lungs in a voxelized model of the XCAT phantom, one nodule per lung, per simulated scan. Voxel values represented attenuation values at 80keV. CT projection data was created by simulating a finite focal spot and using Joseph’s method for forward projection; scanner geometry was that of the Siemens Sensation 64 and the X-ray source was simulated as an 80keV monochromatic beam. Noise realizations were created using Poisson statistics, a realistic bowtie filter and varying tube current. 500 noise realizations were created for the custom TCM designs. All reconstruction was done with FreeCT-wFBP. An SKE/BKE task was used in conjunction with a 2D Hotelling Observer to calculate area-under-the-curve (AUC) as a proxy for “detectability.” AUC was plotted as a function of nodule Z-location to create a “detectability map.” The detectability map for the custom TCM curve was qualitatively assessed relative to previous results for the fixed TC and clinical TCM cases for uniformity. Results: Detection uniformity was improved throughout the mid and lower lungs, however detection remained disproportionately high in the upper lung region. Conclusion: Detection uniformity was improved with a custom TC profile. Future work will incorporate an analytic, task-specific approach to optimize the TC scheme for nodule detection. J. Hoffman: Part-time intern, Toshiba Medical Research Institute; M. McNitt-Gray: Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; F. Noo: Institutional research agreement, Siemens Healthcare; Receives research funding from Siemens Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Impact of compressed breast thickness and dose on lesion detectability in digital mammography: FROC study with simulated lesions in real mammograms

Purpose: The aim of this work was twofold: (1) to examine whether, with standard automatic exposure control (AEC) settings that maintain pixel values in the detector constant, lesion detectability in clinical images decreases as a function of breast thickness and (2) to verify whether a new AEC setup can increase lesion detectability at larger breast thicknesses. Methods: Screening patient images, acquired on two identical digital mammography systems, were collected over a period of 2 yr. Mammograms were acquired under standard AEC conditions (part 1) and subsequently with a new AEC setup (part 2), programmed to use the standard AEC settings for compressed breast thicknesses ≤49 mm, while a relative dose increase was applied above this thickness. The images were divided into four thickness groups: T1 ≤ 29 mm, T2 = 30–49 mm, T3 = 50–69 mm, and T4 ≥ 70 mm, with each thickness group containing 130 randomly selected craniocaudal lesion-free images. Two measures of density were obtained for every image: a BI-RADS score and a map of volumetric breast density created with a software application (VolparaDensity, Matakina, NZ). This information was used to select subsets of four images, containing one image from each thickness group, matched to a (global) BI-RADS score and containing a region with the same (local) VOLPARA volumetric density value. One selected lesion (a microcalcification cluster or a mass) was simulated into each of the four images. This process was repeated so that, for a given thickness group, half the images contained a single lesion and half were lesion-free. The lesion templates created and inserted in groups T3 and T4 for the first part of the study were then inserted into the images of thickness groups T3 and T4 acquired with higher dose settings. Finally, all images were visualized using the ViewDEX software and scored by four radiologists performing a free search study. A statistical jackknife-alternative free-response receiver operating characteristic analysis was applied. Results: For part 1, the alternative free-response receiver operating characteristic curves for the four readers were 0.80, 0.65, 0.55 and 0.56 in going from T1 to T4, indicating a decrease in detectability with increasing breast thickness. P-values and the 95% confidence interval showed no significant difference for the T3-T4 comparison (p = 0.78) while all the other differences were significant (p < 0.05). Separate analysis of microcalcification clusters presented the same results while for mass detection, the only significant difference came when comparing T1 to the other thickness groups. Comparing the scores of part 1 and part 2, results for the T3 group acquired with the new AEC setup and T3 group at standard AEC doses were significantly different (p = 0.0004), indicating improved detection. For this group a subanalysis for microcalcification detection gave the same results while no significant difference was found for mass detection. Conclusions: These data using clinical images confirm results found in simple QA tests for many mammography systems that detectability falls as breast thickness increases. Results obtained with the AEC setup for constant detectability above 49 mm showed an increase in lesion detection with compressed breast thickness, bringing detectability of lesions to the same level.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

HIDES (Hybrid Intrusion Detection for Energy Systems) [SWR-19-65]

Hybrid Intrusion Detection for Energy Systems (HIDES) is a conglomerate detector that incorporates three major components: (1) Signature-based detection that involves information technology (IT) related anomaly detection rules, (2) Behavior-based detection that utilizes specially crafted rulesets for detecting abnormal behavior regarding the power system's supervisory control and data acquisition (SCADA) communication streams, and (3) Learning-based detection that combines SCADA communication with phasor measurements to identify otherwise non-detectible attack vectors. The software itself interconnects intrusion detection engine (IDE) enabling communications between the generated OT and IT alerts with phasor measurements for the learning-based detection system. The resulting detection system can detect a wide array of IT, OT, and physical attacks in a real-time setting.

Rivera, Joshua↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2024

Real World Use Case Evaluation of Radar Retro-reflectors for Autonomous Vehicle Lane Detection Applications

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical R2 score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.

Brown, Nicolas E.↗
DOE OSTI2023

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Gay, Robert S.↗