The optical and scopic urges viewed from a phenomenological perspective

Taking as starting points the books The Address of the Eye: A Phenomenology of Film Experience, by Vivian Sobchak, and Les quatre concepts fondamentaux de la psychanalyse, by Jacques Lacan, this article proposes to look at two well renowned film objects – Rear Window (Alfred Hitchcock, 1954, USA) and Peeping Tom (Michael Powell, 1960, UK) – in order to equate two forms of perception that, all things considered, come together as one: the perception of the mechanical apparatuses that record and project film and the optical and mental apparatuses that operate on the human filmmakers as well as their intradiegetic protagonists. In fact, these two films not only explore the characteristics and limits of vision and affection in their diegetic world, that is part of the filmmaker’s world itself, but reveals just how much the human lives through the eye and the expression of the machine itself. Film ontology is foremost a matter of (re)production rather than creation.

Single side damage simulations and detection in beam-like structures

Beam-like structures are the most common components in real engineering, while single side damage is often encountered. In this study, a numerical analysis of single side damage in a free-free beam is analysed with three different finite element models; namely solid, shell and beam models for demonstrating their performance in simulating real structures. Similar to experiment, damage is introduced into one side of the beam, and natural frequencies are extracted from the simulations and compared with experimental and analytical results. Mode shapes are also analysed with modal assurance criterion. The results from simulations reveal a good performance of the three models in extracting natural frequencies, and solid model performs better than shell while shell model performs better than beam model under intact state. For damaged states, the natural frequencies captured from solid model show more sensitivity to damage severity than shell model and shell model performs similar to the beam model in distinguishing damage. The main contribution of this paper is to perform a comparison between three finite element models and experimental data as well as analytical solutions. The finite element results show a relatively well performance.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.