An Iterative Quality-Based Localization Algorithm for Ad Hoc Networks

An iterative quality-based algorithm for location discovery is presented which can be used in wireless ad hoc sensor networks. The algorithm will take the reliability of measurements into account and will produce a reliability index for every estimated location using a statistical approach. The algorithm can also work in a hybrid network with different kinds of distance measuring techniques. It will use the reliability of each of these methods in the final result. Satisfactory results can be achieved with this approach

Real-time 3-D X-ray and gamma-ray viewer

A multi-pinhole aperture lead screen forms an equal plurality of invisible mini-images having dissimilar perspectives of an X-ray and gamma-ray emitting object (ABC) onto a near-earth phosphor layer. This layer provides visible light mini-images directly into a visible light image intensifier. A viewing screen having an equal number of dissimilar perspective apertures distributed across its face in a geometric pattern identical to the lead screen, provides a viewer with a real, pseudoscopic image (A'B'C') of the object with full horizontal and vertical parallax. Alternatively, a third screen identical to viewing screen and spaced apart from a second visible light image intensifier, may be positioned between the first image intensifier and the viewing screen, thereby providing the viewer with a virtual, orthoscopic image (A"B"C") of the object (ABC) with full horizontal and vertical parallax

Skolem Functions for Factored Formulas

Given a propositional formula F(x,y), a Skolem function for x is a function \Psi(y), such that substituting \Psi(y) for x in F gives a formula semantically equivalent to \exists F. Automatically generating Skolem functions is of significant interest in several applications including certified QBF solving, finding strategies of players in games, synthesising circuits and bit-vector programs from specifications, disjunctive decomposition of sequential circuits etc. In many such applications, F is given as a conjunction of factors, each of which depends on a small subset of variables. Existing algorithms for Skolem function generation ignore any such factored form and treat F as a monolithic function. This presents scalability hurdles in medium to large problem instances. In this paper, we argue that exploiting the factored form of F can give significant performance improvements in practice when computing Skolem functions. We present a new CEGAR style algorithm for generating Skolem functions from factored propositional formulas. In contrast to earlier work, our algorithm neither requires a proof of QBF satisfiability nor uses composition of monolithic conjunctions of factors. We show experimentally that our algorithm generates smaller Skolem functions and outperforms state-of-the-art approaches on several large benchmarks.Comment: Full version of FMCAD 2015 conference publicatio

A comprehensive theory of induction and abstraction, part II

This is part II in a series of papers outlining Abstraction Theory, a theory that I propose provides a solution to the characterisation or epistemological problem of induction. Logic is built from first principles severed from language such that there is one universal logic independent of specific logical languages. A theory of (non-linguistic) meaning is developed which provides the basis for the dissolution of the `grue' problem and problems of the non-uniqueness of probabilities in inductive logics. The problem of counterfactual conditionals is generalised to a problem of truth conditions of hypotheses and this general problem is then solved by the notion of abstractions. The probability calculus is developed with examples given. In future parts of the series the full decision theory is developed and its properties explored

k-Step Relative Inductive Generalization

We introduce a new form of SAT-based symbolic model checking. One common idea in SAT-based symbolic model checking is to generate new clauses from states that can lead to property violations. Our previous work suggests applying induction to generalize from such states. While effective on some benchmarks, the main problem with inductive generalization is that not all such states can be inductively generalized at a given time in the analysis, resulting in long searches for generalizable states on some benchmarks. This paper introduces the idea of inductively generalizing states relative to $k$-step over-approximations: a given state is inductively generalized relative to the latest $k$-step over-approximation relative to which the negation of the state is itself inductive. This idea motivates an algorithm that inductively generalizes a given state at the highest level $k$ so far examined, possibly by generating more than one mutually $k$-step relative inductive clause. We present experimental evidence that the algorithm is effective in practice.Comment: 14 page

Enhancing Semantic Bidirectionalization via Shape Bidirectionalizer Plug-ins

Matsuda et al. (2007) and Voigtlander (2009) have introduced two techniques that given a source-to-view function provide an update propagation function mapping an original source and an updated view back to an updated source, subject to standard consistency conditions. Previously, we developed a synthesis of the two techniques, based on a separation of shape and content aspects (Voigtlander et al. 2010). Here, we carry that idea further, reworking the technique of Voigtlander such that any shape bidirectionalizer (based on the work of Matsuda et al. or not) can be used as a plug-in, to good effect. We also provide a data-type-generic account, enabling wider reuse, including the use of pluggable bidirectionalization itself as a plug-in