GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

(Remix) True Holograms: A Different Kind of Screen; A Different Kind of Ghost

This chapter discusses true holograms as a “remix” discussion from Chapter 4 of ghosts out of the post-screen. Commonly confused with holographic projections, true holograms are two-dimensional images naturally viewed (i.e. without optical aids) as 3-dimensional objects. Leveraging theoretical sources such as Deleuze’s notion of “the brain is the screen” and Vilém Flusser’s ideas of point culture and linearity, the chapter argues for the post-screen through the true hologram whose ghosts are not of the spectral return of the dead, but digital apparitions via which the human mind ideates and projects realities. These digital ghosts thus return with a necromancy of their own on the terms of zerodimensionality and post-rationality, confronting us with new problems of reality and questions about ourselves.

The visual arts

The visual arts, as compared to the performing arts, are defined by their static nature as fixed objects. However, visual art objects often have a ‘dual static/dynamic’ nature that allows them to convey a sense of both motion and emotion, especially when they depict human models. As a result, such objects appear to viewers as frozen snapshots of ongoing actions or gestures. The most art-specific process for the visual arts is the production of two-dimensional images. Compared with the production of three-dimensional objects, two-dimensional images require a dimensional reduction in order to create a flattened representation of a scene on a surface. Drawing is thus the ultimate visual arts activity.

A presentation set of 19th and 20th century photographic processes from the Art Gallery of Ontario's Harris study collection

With the rise in digital photography -- and the shift in photography's materiality -- it has become increasingly important to revisit photographs and reconsider them as multifaceted objects, and not simply as two-dimensional images. In this applied thesis project, the Art Gallery of Ontario's John Richmond Harris study collection was used as the basis for creating a presentation set of fifteen different 19th and 20th century positive photographic processes. With specially constructed archival housings and concise didactic labels accompanying each photograph, this presentation set aims to enable learning about the identification of photographic processes through the use of original examples, while encouraging the consideration of photography's material qualities, to help the user better understand photography's past social and cultural function.

A presentation set of 19th and 20th century photographic processes from the Art Gallery of Ontario's Harris study collection

With the rise in digital photography -- and the shift in photography's materiality -- it has become increasingly important to revisit photographs and reconsider them as multifaceted objects, and not simply as two-dimensional images. In this applied thesis project, the Art Gallery of Ontario's John Richmond Harris study collection was used as the basis for creating a presentation set of fifteen different 19th and 20th century positive photographic processes. With specially constructed archival housings and concise didactic labels accompanying each photograph, this presentation set aims to enable learning about the identification of photographic processes through the use of original examples, while encouraging the consideration of photography's material qualities, to help the user better understand photography's past social and cultural function.

Simulation of Two-Dimensional Images for Ion-Irradiation Induced Change in Lattice Structures and Magnetic States in Oxides by Using Monte Carlo Method

A Monte Carlo method was used to simulate the two-dimensional images of ion-irradiation-induced change in lattice structures and magnetic states in oxides. Under the assumption that the lattice structures and the magnetic states are modified only inside the narrow one-dimensional region along the ion beam path (the ion track), and that such modifications are affected by ion track overlapping, the exposure of oxide targets to spatially random ion impacts was simulated by the Monte Carlo method. Through the Monte Carlo method, the evolutions of the two-dimensional images for the amorphization of TiO2, the lattice structure transformation of ZrO2, and the transition of magnetic states of CeO2 were simulated as a function of ion fluence. The total fractions of the modified areas were calculated from the two-dimensional images. They agree well with the experimental results and those estimated by using the Poisson distribution functions.