Welcome to the UA Campus Repository, a service of the University of Arizona Libraries. The repository shares, archives and preserves unique digital materials from faculty, staff, students and affiliated contributors. Contact us at repository@u.library.arizona.edu with any questions.


Featured submissions

May 2020

  • The Archive and the Guide Series, published by the Center for Creative Photography (CCP), are now available in the repository. The volumes highlight materials in the CCP's research collections.

April 2020

  • Rangelands Volumes 1-38 (1979-2016) are now available in the Campus Repository. These publically available journal archives are made available by the University of Arizona Libraries in partnership with the Society for Range Management.
  • Are you interested in women's history and agriculture in Arizona? Special Collections at the University of Arizona Libraries has digitized Reports of the Home Demonstration Agents. These documents provide a window into Arizona life from 1918-1958.
  • The Arizona Geological Survey continues to add new content, from reports and maps to geospatial data, to the Campus Repository. Explore the latest materials in the AZGS Document Repository.
  • A Deliberate Bit Flipping Coding Scheme for Data-Dependent Two-Dimensional Channels

    Bahrami, Mohsen; Vasic, Bane; Univ Arizona, Dept Elect & Comp Engn (Institute of Electrical and Electronics Engineers (IEEE), 2020-02)
    In this paper, we present a deliberate bit flipping (DBF) coding scheme for binary two-dimensional (2-D) channels, where specific patterns in channel inputs are the significant cause of errors. The idea is to eliminate a constrained encoder and, instead, embed a constraint into an error correction codeword that is arranged into a 2-D array by deliberately flipping the bits that violate the constraint. The DBF method relies on the error correction capability of the code being used so that it should be able to correct both deliberate errors and channel errors. Therefore, it is crucial to flip minimum number of bits in order not to overburden the error correction decoder. We devise a constrained combinatorial formulation for minimizing the number of flipped bits for a given set of harmful patterns. The generalized belief propagation algorithm is used to find an approximate solution for the problem. We evaluate the performance gain of our proposed approach on a data-dependent 2-D channel, where 2-D isolated-bits patterns are the harmful patterns for the channel. Furthermore, the performance of the DBF method is compared with classical 2-D constrained coding schemes for the 2-D no isolated-bits constraint on a memoryless binary symmetric channel.
  • An open, scalable, and flexible framework for automated aerial measurement of field experiments

    Schnaufer, Christophe; Pistorius, Julian L.; LeBauer, David S.; Univ Arizona (SPIE, 2020-05-19)
    Unoccupied areal vehicles (UAVs or drones) are increasingly used in field research. Drones capable of routinely and consistently capturing high quality imagery of experimental fields have become relatively inexpensive. However, converting these images into scientifically useable data has become a bottleneck. A number of tools exist to support this work ow, but there is no framework for making these tools interopreable, sharable, and scalable. Here we present an initial draft of the Drone Processing Pipeline (DPP), a framework for processing agricultural research imagery that supports best practices and interoperability. DPP emphasizes open software and data that can be shared among and used in whole or part by the research community. We are building the DPP as a distributed, scalable, and flexible pipeline for converting drone imagery into orthomosaics, point clouds, and plot level statistics. Our intent is not to replace, but to integrate components from the emerging ecosystem of utilities with a focus on end-to-end automation and scalability. The initial focus of DPP is the measurements of experimental plots in field research. In the future we expect that standardization will enable new scientific discovery by facilitating collaboration and sharing of software and data. Our vision is to create a processing pipeline that is open, flexible, extensible, portable, and automated. With modern tools, deploying a pipeline on a laptop or HPC should only take a single command. Running a pipeline and publishing data should require only input data and a defined work flow.
  • Coyote Papers 22: Frontmatter and TOC

    Nitschke, Remo; Romero Diaz, Damian Y; Powell, John; De la Cruz Sánchez, Gabriela (University of Arizona Linguistics Circle, 2020)
  • Escaping siloed phonology: Framing Irish lenition in Emergent Grammar

    McCullough, Kerry; University of Arizona (University of Arizona Linguistics Circle, 2020)
    Irish displays a complex mutation system in which regular phonological alternations are sensitive to arbitrary morphological information. The Emergent Grammar (EG) model is well-suited to address this phenomenon. This paper details how the model's technology accounts for the phonological regularity and morphological opacity of lenition in Irish.
  • Resistance, Consciousness, and Filipina Hip Hop Identity: A Phonological Analysis

    Tseng, Serene; University of Arizona (University of Arizona Linguistics Circle, 2020)
    In this paper, I investigate the phonology and Hip Hop Language of two Filipina American rappers, Ruby Ibarra and Rocky Rivera, and how they express their understandings of identity and language and race, all in the context of Hip Hop and Asian America.

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