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1. KnitGIST: A Programming Synthesis Toolkit for Generating Functional Machine-Knitting Textures

   https://doi.org/10.1145/3379337.3415590  

   Megan Hofmann, Jennifer Mankoff ( January 2020 , ACM)

   null (Ed.)

   Automatic knitting machines are robust, digital fabrication devices that enable rapid and reliable production of attractive, functional objects by combining stitches to produce unique physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted properties. We present KnitGIST (Generative Instantiation Synthesis Toolkit for knitting), a program synthesis pipeline and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture design. KnitGIST generates a machine-knittable program in a domain-specific programming language. 

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2. Semantics and Scheduling for Machine Knitting Compilers

   https://doi.org/10.1145/3592449  

   Lin, Jenny ; Narayanan, Vidya ; Ikarashi, Yuka ; Ragan-Kelley, Jonathan ; Bernstein, Gilbert ; McCann, James ( August 2023 , ACM Transactions on Graphics)

   Machine knitting is a well-established fabrication technique for complex soft objects, and both companies and researchers have developed tools for generating machine knitting patterns. However, existing representations for machine knitted objects are incomplete (do not cover the complete domain of machine knittable objects) or overly specific (do not account for symmetries and equivalences among knitting instruction sequences). This makes it difficult to define correctness in machine knitting, let alone verify the correctness of a given program or program transformation. The major contribution of this work is a formal semantics for knitout, a low-level Domain Specific Language for knitting machines. We accomplish this by using what we call the "fenced tangle," which extends concepts from knot theory to allow for a mathematical definition of knitting program equivalence that matches the intuition behind knit objects. Finally, using this formal representation, we prove the correctness of a sequence of rewrite rules; and demonstrate how these rewrite rules can form the foundation for higher-level tasks such as compiling a program for a specific machine and optimizing for time/reliability, all while provably generating the same knit object under our proposed semantics. By establishing formal definitions of correctness, this work provides a strong foundation for compiling and optimizing knit programs. 

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3. Unsupervised Learning and Adaptive Classification of Neuromorphic Tactile Encoding of Textures

   https://doi.org/10.1109/BIOCAS.2018.8584702  

   Iskarous, Mark M. ; Nguyen, Harrison H. ; Osborn, Luke E. ; Betthauser, Joseph L. ; Thakor, Nitish V. ( October 2018 , Proceedings of IEEE Biomedical Circuits and Systems)

   null (Ed.)

   In this work, we investigated the classification of texture by neuromorphic tactile encoding and an unsupervised learning method. Additionally, we developed an adaptive classification algorithm to detect and characterize the presence of new texture data. The neuromorphic tactile encoding of textures from a multilayer tactile sensor was based on the physical structure and afferent spike signaling of human glabrous skin mechanoreceptors. We explored different neuromorphic spike pattern metrics and dimensionality reduction techniques in order to maximize classification accuracy while improving computational efficiency. Using a dataset composed of 3 textures, we showed that unsupervised learning of the neuromorphic tactile encoding data had high classification accuracy (mean=86.46%, sd=5 .44%). Moreover, the adaptive classification algorithm was successful at determining that there were 3 underlying textures in the training dataset. In this work, tactile information is transformed into neuromorphic spiking activity that can be used as a stimulation pattern to elicit texture sensation for prosthesis users. Furthermore, we provide the basis for identifying new textures adaptively which can be used to actively modify stimulation patterns to improve texture discrimination for the user. 

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4. Invisible Textures: Comparing Machine and Human Perception of Environment Texture for AR

   https://doi.org/10.1145/3615452.3617937  

   Scargill, Tim ; Hadziahmetovic, Majda ; Gorlatova, Maria ( October 2023 , Proc. ACM ImmerCom)

   Mobile augmented reality (AR) has a wide range of promising applications, but its efficacy is subject to the impact of environment texture on both machine and human perception. Performance of the machine perception algorithm underlying accurate positioning of virtual content, visual-inertial SLAM (VI-SLAM), is known to degrade in low-texture conditions, but there is a lack of data in realistic scenarios. We address this through extensive experiments using a game engine-based emulator, with 112 textures and over 5000 trials. Conversely, human task performance and response times in AR have been shown to increase in environments perceived as textured. We investigate and provide encouraging evidence for invisible textures, which result in good VI-SLAM performance with minimal impact on human perception of virtual content. This arises from fundamental differences between VI-SLAM-based machine perception, and human perception as described by the contrast sensitivity function. Our insights open up exciting possibilities for deploying ambient IoT devices that display invisible textures, as part of systems which automatically optimize AR environments. 

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5. MR method for measuring microscopic histologic soft tissue textures

   https://doi.org/10.1002/mrm.28731  

   Sonn, Geoffrey A. ; Fan, Richard E. ; Kunder, Christian A. ; Gold, Garry E. ; James, Kristin M. ; Parker, Ian D. ; Carlson, Jean M. ; Cannizzaro, Scott M. ; James, Timothy W. ( February 2021 , Magnetic Resonance in Medicine)

   Provide a direct, non‐invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases.

   MR micro‐Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k‐values to characterize texture—as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k‐value phase encode to the excited spins in the VOI, and acquiring each individual k‐value data point in a single excitation—providing motion immunity and extended acquisition time for maximizing signal‐to‐noise ratio. Additional k‐value measurements from the same tissue can be made to characterize the tissue texture in the VOI—there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue.

   Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented.

   The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.

    

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