Randomized Initialization of a Wireless Multihop Network

Address autoconfiguration is an important mechanism required to set the IP address of a node automatically in a wireless network. The address autoconfiguration, also known as initialization or naming, consists to give a unique identifier ranging from 1 to $n$ for a set of $n$ indistinguishable nodes. We consider a wireless network where $n$ nodes (processors) are randomly thrown in a square $X$, uniformly and independently. We assume that the network is synchronous and two nodes are able to communicate if they are within distance at most of $r$ of each other ($r$ is the transmitting/receiving range). The model of this paper concerns nodes without the collision detection ability: if two or more neighbors of a processor $u$ transmit concurrently at the same time, then $u$ would not receive either messages. We suppose also that nodes know neither the topology of the network nor the number of nodes in the network. Moreover, they start indistinguishable, anonymous and unnamed. Under this extremal scenario, we design and analyze a fully distributed protocol to achieve the initialization task for a wireless multihop network of $n$ nodes uniformly scattered in a square $X$. We show how the transmitting range of the deployed stations can affect the typical characteristics such as the degrees and the diameter of the network. By allowing the nodes to transmit at a range r= \sqrt{\frac{(1+\ell) \ln{n} \SIZE}{\pi n}} (slightly greater than the one required to have a connected network), we show how to design a randomized protocol running in expected time $O(n^{3/2} \log^2{n})$ in order to assign a unique number ranging from 1 to $n$ to each of the $n$ participating nodes

Colour Communication Within Different Languages

For computational methods aiming to reproduce colour names that are meaningful to speakers of different languages, the mapping between perceptual and linguistic aspects of colour is a problem of central information processing. This thesis advances the field of computational colour communication within different languages in five main directions. First, we show that web-based experimental methodologies offer considerable advantages in obtaining a large number of colour naming responses in British and American English, Greek, Russian, Thai and Turkish. We continue with the application of machine learning methods to discover criteria in linguistic, behavioural and geometric features of colour names that distinguish classes of colours. We show that primary colour terms do not form a coherent class, whilst achromatic and basic classes do. We then propose and evaluate a computational model trained by human responses in the online experiment to automate the assignment of colour names in different languages across the full three-dimensional colour gamut. Fourth, we determine for the first time the location of colour names within a physiologically-based cone excitation space through an unconstrained colour naming experiment using a calibrated monitor under controlled viewing conditions. We show a good correspondence between online and offline datasets; and confirm the validity of both experimental methodologies for estimating colour naming functions in laboratory and real-world monitor settings. Finally, we present a novel information theoretic measure, called dispensability, for colour categories that predicts a gradual scale of basicness across languages from both web- and laboratory- based unconstrained colour naming datasets. As a result, this thesis contributes experimental and computational methodologies towards the development of multilingual colour communication schemes

Augmenting a colour lexicon

Languages differ markedly in the number of colour terms in their lexicons. The Himba, for example, a remote culture in Namibia, were reported in 2005 to have only a 5-colour term language. We re-examined their colour naming using a novel computer-based method drawing colours from across the gamut rather than only from the saturated shell of colour space that is the norm in cross-cultural colour research. Measuring confidence in communication, the Himba now have seven terms, or more properly categories, that are independent of other colour terms. Thus, we report the first augmentation of major terms, namely green and brown, to a colour lexicon in any language. A critical examination of supervised and unsupervised machine-learning approaches across the two datasets collected at different periods shows that perceptual mechanisms can, at most, only to some extent explain colour category formation and that cultural factors, such as linguistic similarity are the critical driving force for augmenting colour terms and effective colour communication