Pulse-firing winner-take-all networks

Winner-take-all (WTA) neural networks using pulse-firing processing elements are introduced. In the pulse-firing WTA (PWTA) networks described, input and activation signal shunting is controlled by one shared lateral inhibition signal. This organization yields an O(n) area complexity that is convenient for integrated circuit implementation. Appropriately specified network parameters allow for the accurate continuous evaluation of inputs using a signal representation compatible with established pulse-firing neural network implementations

UR-46 BreastNet;

In the United states, 13% of women are diagnosed with breast cancer in their lifetime, and it is the second leading cause of death by cancer in women. Early detection and screening can result in an increase of life expectancy by 10 years on average. Unfortunately, breast cancer can be challenging to detect, since it can appear anywhere in the breast. Cancer that is detected in its early stages can give patients more options and save thousands of dollars in medical costs. Some of the most recent developments in computer science and machine learning are in the biomedical field, especially individualized healthcare. There is also an increase in the demand for telehealth options, reducing healthcare costs. With the help of computational technology, medical practitioners will be able to process data more quickly, which will allow more patients to have access to reliable treatment. Besides, systematic processes for interpreting various data types (such as clinical features, genetic information, and medical images) can identify trends that a human eye would not detect. This project aims to design and implement an artificial intelligence-based model called BreastNet to classify breast cancer into high and low-risk categories based on a combination of MRI images and clinical data. BreastNet uses a convolutional neural network (CNN), a type of machine learning methodology that imitates how the human brain learns information. Neurons fire in a connected pathway, reinforcing the relationship between a stimulus and the correct outcome. In this case, the CNN identifies characteristic features within the MRI that correspond to different life expectancy outcomes, which are notated in the clinical data. The clinical data serves as a loss function, which allows the network to identify how well the current model performs on images. We will evaluate the model by dividing the dataset into three partitions: training, validation, and testing, and then uses the evaluation metrics of Accuracy, Loss, F1 Score, Precision, Recall, Specificity, and Sensitivity.Advisors(s): Dr. Mohammed AledhariTopic(s): Artificial IntelligenceCS 426

Analysis of WFPC-2 Core Samples for MMOD Discrimination

An examination of the Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC-2) radiator assembly was conducted at NASA Goddard Space Flight Center during the summer of 2009. Immediately apparent was the predominance of impact features, identified as simple or complex craters, resident only in the thermal paint layer; similar features were observed during a prior survey of the WFPC-1 radiator. Larger impact features displayed spallation zones, darkened areas, and other features not observed in impacts onto bare surfaces. Craters were extracted by coring the radiator in the NASA Johnson Space Centers Space Exposed Hardware cleanroom and were subsequently examined using scanning electron microscopy/energy dispersive X-ray spectroscopy to determine the likely origin, e.g., micrometeoritic or orbital debris, of the impacting projectile. Recently, a selection of large cores was re-examined using a new technique developed to overcome some limitations of traditional crater imaging and analysis. This technique, motivated by thin section analysis, examines a polished, lateral surface area revealed by cross-sectioning the core sample. This paper reviews the technique, the classification rubric as extended by this technique, and results to date

The NASA Orbital Debris Engineering Model 3.1: Development, Verification, and Validation

The NASA Orbital Debris Program Office has developed the Orbital Debris Engineering Model (ORDEM) primarily as a tool for spacecraft designers and other users to understand the long-term risk of collisions with orbital debris. The newest version, ORDEM 3.1, incorporates the latest and highest fidelity datasets available to build and validate representative orbital debris populations encompassing low Earth orbit (LEO) to geosynchronous orbit (GEO) altitudes for the years 2016-2050. ORDEM 3.1 models fluxes for object sizes > 10 m within or transiting LEO and > 10 cm in GEO. The deterministic portion of the populations in ORDEM 3.1 is based on the U.S. Space Surveillance Network (SSN) catalog, which provides coverage down to approximately 10 cm in LEO and 1 m in GEO. Observational datasets from radar, in situ, and optical sources provide a foundation from which the model populations are statistically extrapolated to smaller sizes and orbit regions that are not well-covered by the SSN catalog, yet may pose the greatest threat to operational spacecraft. Objects in LEO ranging from approximately 5 mm to 10 cm are modeled using observational data from ground-based radar, namely the Haystack Ultrawideband Satellite Imaging Radar (HUSIR formerly known as Haystack). The LEO population smaller than approximately 3 mm in size is characterized based on a reanalysis of in situ data from impacts to the windows and radiators of the U.S. Space Transportation System orbiter vehicle, i.e., the Space Shuttle. Data from impacts on the Hubble Space Telescope are also used to validate the sub-millimeter model populations in LEO. Debris in GEO with sizes ranging from 10 cm to 1 m is modeled using optical measurement data from the Michigan Orbital DEbris Survey Telescope (MODEST). Specific, major debris-producing events, including the Fengyun-1C, Iridium 33, and Cosmos 2251 debris clouds, and unique populations, such as sodium-potassium droplets, have been re-examined and are modeled and added to the ORDEM environment separately. The debris environment greater than 1 mm is forecast using NASAs LEO-to- GEO ENvironment Debris model (LEGEND). Future explosions of intact objects and collisions involving objects greater than 10 cm are assessed statistically, and the NASA Standard Satellite Breakup Model is used to generate fragments from these events. Fragments smaller than 10 cm are further differentiated based on material density categories, i.e., high-, medium-, and low-density, to better characterize the potential debris risk posed to spacecraft. The future projection of the sub-millimeter environment is computed using a special small-particle degradation model where small particles are created from intact spacecraft and rocket bodies. This work discusses the development, features, and capabilities of the ORDEM 3.1 model; the ne new data analyses used to build the model populations; and sample verification and validation results

Interpretation of Impact Features on the Surface of the WFPC-2 Radiator

An examination of the Hubble Space Telescope (HST) Wide Field Planetary Camera 2 (WFPC-2) radiator assembly was conducted at NASA Goddard Space Flight Center (GSFC) during the summer of 2009. Immediately apparent was the predominance of impact features resident only in the thermal paint layer; similar phenomenology was observed during a prior survey of the WFPC-1 radiator. As well, larger impact features displayed spallation zones, darkened areas, and other features not encountered in impacts onto bare surfaces. Whereas the characterization of impact features by depth and diameter on unpainted surfaces has been long established, the mitigation provided by the painted layer presented a challenge to further analysis of the WFPC-2 features; a literature search revealed no systematic characterization of the ballistic limit equations of painted or coated surfaces. In order to characterize the impactors responsible for the observed damage, an understanding of the cratering and spallation phenomenology of the painted surface was required. To address that challenge, NASA sponsored a series of hypervelocity calibration shots at the White Sands Test Facility (WSTF). This effort required the following activities: the production, painting, and artificial ageing of test coupons in a manner similar to the actual radiator; the determination of the test matrix parameters projectile diameter and material (mass density), impact velocity, and impact angle, so as to enable both an adequate characterization of the impact by projectile and impact geometry and support hydrocode modeling to fill in and extend the applicability of the calibration shots; the selection of suitable projectiles; logistics; and an analysis of feature characteristics upon return of the coupons. This paper reports the results of the test campaign and presents ballistic limit equations for painted surfaces. We also present initial results of our interpretation methodologies

Investigation of Oxidation Profile in PMR-15 Polyimide using Atomic Microscope (AFM)

Nanoindentation measurements are made on thermosetting materials using cantiever deflection vs. piezoelectric scanner position behavior determined by AFM. The spring model is used to determine mechanical properties of materials. The generalized Sneddon's equation is utilized to calculate Young's moduli for thermosetting materials at ambient conditions. Our investigations show that the force-penetration depth curves during unloading in these materials can be described accurately by a power law relationship. The results show that the accuracy of the measurements can be controlled within 7%. The above method is used to study oxidation profiles in Pl\1R-15 polyimide. The thermo-mechanical profiles ofPNIR-15 indicate that the elastic modulus at the surface portion of the specimen is different from that at the interior of the material. It is also shown that there are two zones within the oxidized portion of the samples. Results confirm that the surface layer and the core material have substantially different properties

Handbook of Research on Transforming Teachers’ Online Pedagogical Reasoning for Engaging K-12 Students in Virtual Learning

Nicole Fletcher (with Candace Joswick and Audrey Meador) is a contributing author, Transforming K-12 Mathematics Classroom Teacher Pedagogy Through Virtual Number Talks, Chapter 20, pp. 402-422. The COVID-19 pandemic drastically transformed the classroom by keeping students and teachers apart for the sake of safety. As schools emptied, remote learning rapidly expanded through online services and video chatrooms. Unfortunately, this disrupted many students and teachers who were not accustomed to remote classrooms. This challenge has forced K-12 teachers to think differently about teaching. Unexpectedly and with little time to prepare, they have been confronted with redesigning their curriculum and instruction from face-to-face to online virtual classrooms to protect students from the COVID-19 virus while ensuring that these new online initiatives remain sustainable and useful in the post-pandemic world. As teachers learn to take advantage of the affordances and strengths of the multiple technologies available for virtual classroom instruction, their instruction both in online and face-to-face will impact what and how students learn in the 21st century. The Handbook of Research on Transforming Teachers’ Online Pedagogical Reasoning for Engaging K-12 Students in Virtual Learning examines the best practices and pedagogical reasoning for designing online strategies that work for K-12 virtual learning. The initial section provides foundational pedagogical ideas for constructing engaging virtual learning environments that leverage the unique strengths and opportunities while avoiding the weaknesses and threats of the online world. The following chapters present instructional strategies for multiple grade levels and content areas: best practices that work, clearly describing why they work, and the teachers’ pedagogical reasoning that supports online implementations. The chapters provide ways to think about teaching in virtual environments that can be used to guide instructional strategy choices and recognizes the fundamental differences between face-to-face and virtual environments as an essential design component. Covering such topics as K-12 classrooms, pedagogical reasoning, and virtual learning, this text is perfect for professors, teachers, students, educational designers and developers, instructional technology faculty, distance learning faculty, and researchers interested in the subject.https://digitalcommons.fairfield.edu/education-books/1071/thumbnail.jp

Solar-heated new technology house

"In 1979, the UMC College of Agriculture began construction of a solar-heated home on a University farm near Columbia. It's called a New Technology House because it incorporates the latest technology available for home construction. The major emphasis is on energy conservation and solar heating. The house was completed in January 1980, and was occupied shortly thereafter by a family of four."--First page.C.L. Day and N.F. Meador (Department of Agricultural Engineering, College of Agriculture)New 9/83/10