46 research outputs found
An Inverse Kinematic Approach Using Groebner Basis Theory Applied to Gait Cycle Analysis
Get PDFKinematics of the human body was researched for the purposes of this study. The force protection issues of today was the motivation to research pattern recognition in the human gait cycle to identify individuals carrying a concealed load on their body. The goal of this research was to identify gait signatures of human subjects and distinguish between subjects carrying a concealed load to subjects without load. Thus, this research was focused on studying the human gait cycle as well as methods used in identifying gait signatures. The main focus herein is concerned with the movement of the lower extremities, in particular, the placement of the foot and how the joint angles area affected with carrying extra load on the body. A method of Inverse Kinematics (IK) using Groebner Basis (GB) Theory is developed to a model of the lower extremities to determine all the solutions of the joint angles, given the position and orientation of the foot. The human gait cycle was captured and analyzed using a VICON Motion capture system. This research highlights the results obtained from applying the method of IK, using GB, to the lower limbs of a human gait cycle to extract and identify gait signatures
A New Paradigm for End-to-End Modeling of Radiometric Instrumentation Systems
No full textEarth observing instruments, such as those embarked on the Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth's Radiant Energy System (CERES), have been used to monitor the arriving solar and the upwelling solar reflected and longwave emitted radiation from low Earth orbit for the past three decades. These instruments have played a crucial role in studying the Earth's radiation budget and developing a decadal climate data record. Prior to launch, these instruments go through several robust design phases followed by rigorous ground calibration campaigns to establish their baseline characterization spectrally, spatially, temporally, and radiometrically. The knowledge gained from building and calibrating these instruments has aided in technology advancements as the need for developing more accurate instruments has increased. In order to understand the prelaunch performance of these instruments, NASA's Langley Research Center (LaRC) has partnered with the Thermal Radiation Group at Virginia Tech to develop first-principle, dynamic electrothermal, numerical models of scanning radiometers that can be used to enhance the understanding of such instruments. The body of research presented here documents the construction of these models by highlighting their development and results and possible applications to the next generation of Earth radiation budget instrument. Much of the effort reported here is based on the author's contribution to NASA's now-deselected Radiation Budget Instrument (RBI) project.Doctor of PhilosophyEarth Radiation Budget (ERB) sensors, such as the Earth Radiation Budget Experiment (ERBE) and the Clouds and the Earth's Radiant Energy System (CERES) have been a crucial part of studying the Earth's radiation budget for the past three decades. The Earth's radiation budget is the natural balance that exists between the energy received from the Sun and the energy radiated back into space. These instruments, which measure the radiative energy arriving and leaving at the top of the Earth's atmosphere, enhance understanding of the roles played by clouds and aerosols in reflecting and absorbing energy, thereby cooling or heating the planet. In order to enable the design for the next-generation Earth radiation budget sensors, NASA Langley has partnered with the Thermal Radiation Group at Virginia Tech to develop a capability for high-fidelity computer modeling that permits the complete characterization of an Earth radiation budget instrument. The resulting simulation consists of computer models for optical components, calibration targets, detecting elements and a source that includes information on anisotropy of a given Earth scene-type (clear vs. cloudy scene, ocean, desert, etc.). The modeling tool permits simulation of the entire science data stream as photons entering the instrument are converted to digital counts leaving the instrument, and provides the flexibility to observe various scene-types whether they be calibration targets or Earth scenes. This dissertation highlights the construction of this modeling tool and its capabilities as it is applied to NASA's now-deselected Radiation Budget Instrument
Enhancing the Ground Calibration in the Short-wavelength Region to Improve Traceability within the Reflected Solar Bands of the CERES Instrument
Get PDFThe Clouds and Earth’s Radiation Energy System (CERES) program produces data of solar-reflected and earth-emitted radiation from the top-of-atmosphere (TOA), within the atmosphere, and at the Earth’s surface. This program currently employs six instruments onboard four different spacecraft to measure and produce long-term record of the earth’s energy budget. CERES Flight Model 6 (FM6), the instrument that will fly on the Joint Polar Satellite System (JPSS) -1 spacecraft, is a scanning broadband radiometer consisting of three sensors that measure three different spectral radiances: solar region consisting of wavelength between 0.3 to 5 microns, total region between 0.3 to 200 microns, and a broadband region of 5 to 40 microns. Rigorous pre-launch ground calibration is performed on these sensors to meet accuracy requirements of 1% and 0.5% for shortwave and longwave radiance observations, respectively.
A Cryogenically cooled Transfer Active Cavity Radiometer (TACR), one of the components of the pre-launch ground calibration facility, was modified in efforts to improve traceability within the reflected solar bands (shortwave and total channels). These modifications included replacing the heritage mirrors from enhanced silver coating to protected aluminum, which in turn resulted in a greater signal-to-noise ratio. These findings along with efforts to quantify the spectral response of the new TACR’s optics from UV to IR are discussed in this paper
Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network
No full textNarrow field-of-view scanning thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO). Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic. Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit. The current contribution considers a fourth possibility, that of an imager operating in LEO. The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array. At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane. Presented is a full-field focusing algorithm based on an artificial neural network (ANN). Absorbed power distributions on the microbolometer array produced by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager. The resulting readout array/scene pairs are then used to train an ANN. We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene. This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager
The Visitors
No full text157 pagesMFA theses in English Language and Literature are not available for direct download. Users wishing to access an MFA thesis in this collection may request access by clicking the link to the restricted file(s) and completing the request form. If we have contact information for the author, we will contact them and request permission to provide access. If we do not have contact information or the author denies or does not respond to our inquiry, we will not be able to provide access.A novel that follows four women from Karachi, Pakistan, estranged friends who come back together in the wake of a tragedy in one of their lives.10000-01-0
First-Principle Dynamic Electro-Thermal Numerical Model of a Scanning Radiometer for Earth Radiation Budget Applications
No full textDiffraction and Polarization Effects in Radiation Heat Transfer: A Case Study
No full textThe Monte-Carlo ray-trace (MCRT) method is particularly well suited to the optical design of instrumentation in which very small radiant signals must be separated from a strong background. The present contribution explores an important application lying at the intersection of physical optics and radiation heat transfer. Specifically, we consider instruments intended to monitor the planetary energy budget from low earth orbit. To accommodate the increasingly exigent accuracy requirements imposed by the Earth science community, it has become necessary to include effects such as diffraction and polarization that are normally omitted in traditional radiation heat transfer modeling. This requires that the usual concept of a “ray” be extended to include wavelength, a phase angle, and polarization. A realistic instrument concept is considered that fully exercises the ability of such an approach to capture optical effects that are either ignored or assessed “offline” in traditional modeling efforts. Investigated is the range of variation of detector illumination when the effects of the source spectral content, diffraction, and polarization are included.</jats:p
V kolì Andriâ Šeptic'kogo : Ukraïns'ke kul'turne vìdrodžennâ za časìv mecenatstva mitropolita
No full textTekst jest pierwszym w języku polskim studium o mecenacie Andrzeja Szeptyckiego (1865-1944), greckokatolickiego metropolity lwowskiego (1900-1944). Autor opisuje dzieła Szeptyckiego: szkolę ikon, Narodowe Muzeum Ukraińskie we Lwowie, mecenat nad ukraińską sztuką nowoczesną, ukraińskie organizacje artystyczne (SPUOM, GDUM, ANUM) i wystawy sztuki ukraińskiej w czasie II wojny światowej we Lwowie. Tekst zawiera unikatowy wykaz ok. 150 artystów ukraińskich z kręgu Andrzeja Szeptyckiego.The text is the first study in Polish on art patronage by Andrej Sheptyckyj (1865-1944), the Greek Catholic metropolite of Lwow (1900-1944). The author describes Sheptyckyj's works: the school of icons, the National Ukrainian Museum in Lwow, patronage for Ukrainian modern art, Ukrainian artistic organizations (SPUOM, GDUM, ANUM) and Ukrainian art exhibitions in Lwow in the time of the World War II. The text contains the unique register (the list) of circa 150 Ukrainian artists from the Andrej Sheptyckyj's circle
Alienation and the Dilemma of Man in Eugene O’Neill’s The Hairy Ape
Get PDFEugene O\u27 Neill, an American playwright was born into a troubled and an upset family on October 16, 1888. Eugene O’ Neill had a quite precarious, wobbly and uneven adolescence as his elder brother was an affirmed alcoholic whereas his mom was a drug addict. This research paper analyzes the alienation, dilemma and the futile struggle of man in the quest of his identity. O\u27Neill followed the course of a superior and advanced writer looking for a profound focus in the entirety of his significant works. His perspective on humankind in his dramatizations is basically sad and heartbreaking. The author needed to cause man to feel free from all worries and inhale outside fresh air and build up a feeling of having a place in the general public in which he lived. However, it was impractical
Automated Segmentation of Metastatic Lymph Nodes in Lymphoma Patients
No full textVed å bruke det kunstige nevrale nettverket 2D U-Net, tester denne masteroppgaven nøyaktigheten til 2D U-Net med formålet om å automatisk segmentere ondartede svultser i PET/MR-bilder av pasienter med metastatisk lymfom.
For Hodgkin- og Non-Hodgkin-lymfom er FDG PET/MR-segmenteringene viktige for prognose, stadieinndeling (staging) og responsvurdering av lymfompasienter. Manuelle segmenteringer er imidlertid tidkrevende og vanskelige i komplekse pasienttilfeller der en har høy sykdomsbyrde. Målet med dette prosjektet er å utvikle en automatisert metode for segmentering av kreftrammede lymfeknuter i PET/MR ved bruk av dyp læring (deep learning), nærmere bestemt et dypt kunstig nevralt nettverk. FDG PET/MR-baseline, interim- og behandlingsavslutningsbilder (EOT) av Hodgkin- og Non-Hodgkin-lymfompasienter ble analysert. To grupper radiologer og nukleærmedisinere har bidratt med klinisk lesing av PET/MR-bildene etter standardiserte protokoller. Imidlertid manglet de faktiske segmenteringene, m.a.o segmenterings fasiten, fra lymfomdatasettet, og disse var avgjørende for å implementere dyplæringsnettverket for en automatisert segmenteringsprosess. De manuelle segmenteringene som krevdes ble utført av forfatteren og validert av en nukleærmedisiner fra St.Olavs Hospital.
Den nevrale nettverksmodellen ble lært hvordan man utfører klassifiseringsoppgaver direkte fra bilder, dvs. nettverket ble opplært til å gjenkjenne mønstre fra et datasett bestående av 64 PET/MR-undersøkelser. Et 3-kanals multimodalt bilde, et RGB-bilde, bestående av PET, T2-HASTE og DWI med b = 800 s/mm^{2} ble brukt som input for algoritmen, og modellen ble lært til å gjenskape segmenteringene i grunnsannheten (segmenterings fasiten) ved å bruke en 2D U-Net-arkitektur.
Videre ble lymfomdatasettet delt inn i et 85/15-forhold for trening og testing som bestod av henholdsvis 53 og 11 PET/MR-undersøkelser. Både en 4-fold og 13-fold kryssvalidering ble utført for opplæringen av modellen. Valideringen resulterte i gjennomsnittlige Dice-score (overlappingsmål) på henholdsvis 0,61 og 0,63 for 4-fold og 13-fold modellene. Flere andre evaluaringer som tap, nøyaktighet, presisjon, tilbakekalling, negativ prediktiv verdi (NPV) og spesifisitet ble inkludert for voxel-nivåanalysen. Resultatene var 0,011, 0,97, 0,83, 0,11, 0,97, 0,99, henholdsvis for 4-fold valideringen og 0,065, 0,97, 0,90, 0.10, 0,97, 0,99, henholdsvis for 13-fold valideringen. Den gjennomsnittlige dice scoren til testpasientene var henholdsvis 0,29 og 0,32 for 4-fold og 13-fold kryssvalidering, noe som antyder en dårligere ytelse på nye og usette pasienter sammenlignet med PET/MR-undersøkelsene brukt i valideringen. Til tross for de generelt høye verdiene for evalueringsmetodene, ga den voxelbaserte analysen ingen god indikasjon på hvor nøyaktig modellen klarte å segmentere kreftlesjoner ettersom flertallet av vokslene i pasientene ble klassifisert som ekte negative (TN). Derfor ble det utført en lesjonsbasert analyse, og den avslørte at modellen ofte segmenterte færre lesjoner enn det som var tilstede i grunnsannheten. Dette indikerte at modellens hovedbegrensning var antallet falske negative predikerte kreftsvultser. Som en konsekvens, presterer modellen bedre på valideringsdataene enn for testdatasettet som ble ekskludert fra opplæringen.
For å konkludere, så segmenterer den trente 2D U-Net-modellen automatisk ondartede lymfeknutesvultser i 3-kanals multimodale bilder. Fremtidig arbeid bør fokusere på å forbedre overlappingsmålet dice score, co-registreringen, redusere antall uoppdagede tumorlesjoner, samt øke datasettet for å sikre en større variasjon i kohorten. Dette kan forbedre både treningen og resultatene.Using the deep learning artificial neural network 2D U-Net, this project tests the accuracy of the 2D U-Net for the purpose of automatically segmenting malignant lesions in PET/MR images of patients with metastatic lymphoma.
For Hodgkin and Non-Hodgkin lymphoma, the FDG PET/MRI segmentations are important for prognosis, staging, and response assessment of lymphoma patients. However, manually segmentations are time-consuming and difficult in complex patient cases and for high disease burden. The aim of this project is to develop an automated method for segmentation of cancer-affected lymph-nodes in PET/MRI using a deep neural network.
FDG PET/MRI baseline, interim, and End-Of-Treatment (EOT) images of Hodgkin and Non-Hodgkin lymphoma patients were analyzed. Two groups of radiologist and nuclear medicine physicians have contributed with clinical reading of the PET/MR images following standardized protocols. However, the segmentation ground truth was missing from the lymphoma dataset, and it was crucial for implementing the deep learning network for an automated segmentation process. The manual segmentation required has therefore been performed by the author and validated by a nuclear medicine physician from St. Olavs Hospital.
The neural network model was taught how to perform classification tasks directly from images, i.e., the network was trained to recognize patterns from a dataset consisting of 64 PET/MRI examinations. A 3-channel multi-modal image, i.e., an RGB image, consisting of a PET, a T2-HASTE, and a DWI with b = 800 s/mm^{2} was used as input for the algorithm. The model was trained to replicate the segmentations of the ground truth by using a 2D U-Net architecture.
Furthermore, the lymphoma dataset was divided in a 85/15 ratio for training and testing consisting of 53 and 11 PET/MRI examinations, respectively. Both a 4-fold and 13-fold cross-validation were performed for the training of the model. The validation resulted in average dice scores of 0.61 and 0.63 respectively for the 4-fold and 13-fold trained models. Several other metrics such as loss, accuracy, precision, recall, Negative Predictive Value (NPV), and specificity were included for the voxel level analysis. The scores were 0.011, 0.97, 0.83, 0.11, 0.97, 0.99, respectively for the 4-fold validation and 0.065, 0.97, 0.90, 0.10, 0.97, 0.99, respectively for the 13-fold validation. The average dice score of the testing patient were 0.29 and 0.32 respectively for the 4-fold and 13-fold cross-validation which suggested an inferior performance on unseen patients compared to the PET/MRI examinations used in the validation. Despite the overall high scores for the evaluation metrics, the voxel based analysis did not give a great indication of how well the model managed to segment cancer lesions due to the majority of the voxels in a patient being classified as true negative. Therefore, a lesion-based analysis were conducted and it revealed that the model often segmented fewer lesions than in the ground truth. This indicated that the model's main limitation was the number of false negative predicted lesions. As a consequence, the model performs better on the validation data than for the testing dataset which was excluded from the training.
In conclusion, the trained 2D U-Net model automatically segments malignant lymph node lesions in the 3-channel multi-modal images. However, future research should focus on improving the dice score, co-registration, decrease the number of undetected tumor lesions, and increase the dataset to ensure a larger variation in the cohort. This will benefit the training and yield better results
