Nesting the Spectacle: A Study of Toronto's New Opera House, an Architecture that Averts Being Iconic

The Four Seasons Centre for the Performing Arts, Toronto's first purpose-built opera facility, was constructed in the early 2000s as part of a substantial investment in the city's cultural infrastructure that brought about a short-lived period of developing cultural buildings, commonly known as Toronto's Cultural Renaissance. For developing the opera facility, the Canadian Opera Company pursued a strategy that marked a distinction from the approach of many of its local and international rivals. Instead of commissioning celebrity architects for a spectacular iconic design — a strategy that, by the end of the twentieth century, had become part of a prevalent trend of developing cultural buildings all around the world — the Opera Company strove to build a humble-looking structure, designed by a Toronto-based architectural firm. Against the backdrop of Toronto's Cultural Renaissance developments, which involved the construction of a few world-class urban icons in the city, this research explores the reasons behind the Opera Company's distinct approach. While critical studies concerned with positioning architecture in its socio-political context often concentrate on interrogating cases of iconic buildings, especially since such architectural products are generally considered the most likely outcome of conditions of neoliberal globalization, this work contends that iconic architecture is not the only manifestation of the appropriation of architecture by the powerful. By borrowing from Bourdieu's theory of practice, it points to the implicit nature of architecture's complicity in processes of power and explores alternative pathways through which architecture can retain capitalist interests in urban space. The research emphasizes the importance of investigating architectural products that are considered banal and ordinary, especially since such cases have often remained at the margins of critical examination

RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverage estimation. Conducting site surveys to obtain RF maps is labor-intensive and sometimes not feasible. In this paper, we propose radio-frequency adversarial deep-learning inference for automated network coverage estimation (RADIANCE), a generative adversarial network (GAN) based approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a semantic map, a high-level representation of the indoor environment to encode spatial relationships and attributes of objects within the environment and guide the RF map generation process. We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment. RADIANCE incorporates this loss function along with the antenna pattern to capture signal propagation within a given indoor configuration and generate new patterns under new configuration, antenna (beam) pattern, and center frequency. Extensive simulations are conducted to compare RADIANCE with ray-tracing simulations of RF maps. Our results show that RADIANCE achieves a mean average error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity index (MS-SSIM) of 0.80.Comment: 6 pages, 6 figure