Signals in the Soil: An Introduction to Wireless Underground Communications

In this chapter, wireless underground (UG) communications are introduced. A detailed overview of WUC is given. A comprehensive review of research challenges in WUC is presented. The evolution of underground wireless is also discussed. Moreover, different component of UG communications is wireless. The WUC system architecture is explained with a detailed discussion of the anatomy of an underground mote. The examples of UG wireless communication systems are explored. Furthermore, the differences of UG wireless and over-the-air wireless are debated. Different types of wireless underground channel (e.g., In-Soil, Soil-to-Air, and Air-to-Soil) are reported as well

Online Learning From Finite Training Sets and

this paper, we give an exact analysis of online learning in a simple model system. Our aim is twofold: (1) to assess how the combination of non-infinitesimal learning rates j and finite training sets (containing ff examples per weight) affects online learning, and (2) to compare the generalization performance of online and offline learning. A priori, one Online learning can also be used to learn teacher rules that vary in time. The assumption of an infinite set (or `stream') of training examples is then much more plausible, and in fact necessary for continued adaptation of the student. We do not consider this case in the followin

Collaborative Recommendation:

this article is organised as follows. We begin with a discussion of collaborative recommendation and a formalisation of the notions of robustness and the perturbations with which we are concerned (Section 2). We then analyse robustness from both the accuracy and stability perspectives. Regarding accuracy, in Section 3 we formalise robustness in machine learnings terms, and introduce a novel form of class noise that models an interesting suite of attacks. We develop two models that predict the change in accuracy as a function of the number of fake ratings that have been inserted into the customer/product matrix. Regarding stability, we present a framework that describes the stability of a recommendation system subjected to various forms of attack (Section 4). In both cases, we empirically evaluate our predications against several real-world data-sets. We conclude with a description of related work (Section 3.4) and with a summary of our results and a discussion of several open issues (Section 5