Search

This dissertation reflects on the methods and concepts employed in constructing a history of digital architecture. By focusing on the methodological issues, it complements and expands the research developed for the monographic study Digital Architecture Beyond Computers (DABC) and the book chapter "Crypto Architecture". In both pieces digital architecture is understood to cover a period of time that stretches well beyond the appearance of the modern digital computer (after World War Two). The notion of computing numbers and symbols to apprehend and intervene in our reality is in fact a much older idea than the invention of the modern digital computer. This dissertation reflects on the approach suggested by both writings by analysing the conceptual basis of computation in order to devise an appropriate historiographic approach to digital architecture. The aim of the investigation is to move beyond a technologically‐driven, utilitarian view of computation in favour of a more conceptual position that foregrounds computation's fundamental logic and the role of the disciplines that informed and continue to inform it. This broader perspective aims at establishing a relation between the artifacts and the processes of digital architecture; that is, between what digital architecture is (which DABC explores through case studies in which computation and design affected one another), and how it is generated (the techniques and methods deployed to design architecture). This dissertation introduces a specific conceptual figure to articulate the historiography of digital architecture: the cypher. Cyphers address the fundamental challenges emerging from constructing a history of digital architecture, they organise the vast collections of case studies forming the history of digital architecture, foreground the conceptual motivations behind computation, and acknowledge the role that different disciplines (philosophy, logic, semiotic) have played in shaping what we call digital architecture.

The chapters of this thesis analyse elements of households' consumption, labour supply and saving decisions within the life cycle framework. The focus is on three main issues arising in this context. The first considers modelling both a durable good (housing) and labour supply choices together with life cycle choices over consumption and sav ings. The importance of modelling these features together comes from the existence of explicit earnings-related borrowing constraints when taking out a mortgage. Empiri cal evidence is provided for the UK and the modelling exercise, although not aimed to reproduce the evidence of a single country, is calibrated to the UK. The second issue concerns incorporating expectations into the model. Two ap proaches axe followed in the different chapters. One approach infers expectations from past realisations. The alternative approach uses expectations elicited in survey inter views. The third issue relates to whether individuals save enough for their retirement. This question is addressed by using expectations on retirement outcomes, elicited directly in accordance with the approach mentioned above, and collected for a representative sample of the Italian working population. Data of interest to our exercise is for the years before and after a series of major pension reforms.

On average four people get killed and 90 people get seriously injured on Australian roads every day, according to research conducted by the Australian government. Intelligent systems can help to reduce the number of accidents on the roads by creating tools to monitor the driver’s behaviour according to the lane path. Lane detectors are important tools and many approaches have been applied to solve the lane detection problem, but most of them are not capable of dealing with dynamic lighting efficiently. The current approaches still rely in expensive computational effort and long training phases. The reliability of the lane detection is a key component for safe navigation on the roads and an automatic solution becomes an interesting topic to explore. This research proposes a vision-based software architecture which combines an appearance-based analysis with salient point tracking, automatically adapting to different lighting situations. It aims to increase the efficiency and accuracy for the detection of the left- or rightmost lane boundary (ego-lane) providing a flexible software architecture to support drivers and to be extended to driverless vehicle control. The experiments exposed a series of important facts to address. First, the adaptive appearance-based analysis is capable of segmenting multiple lane painting marks. Second, the system can reduce the false positive detection by using an efficient road model. The proposed model guides the image processing cues to focus on relevant Regions-Of-Interest (ROIs) for lane marking detection instead of the analysing the whole image.