The Relation Between Offset and Conchoid Constructions

The one-sided offset surface Fd of a given surface F is, roughly speaking, obtained by shifting the tangent planes of F in direction of its oriented normal vector. The conchoid surface Gd of a given surface G is roughly speaking obtained by increasing the distance of G to a fixed reference point O by d. Whereas the offset operation is well known and implemented in most CAD-software systems, the conchoid operation is less known, although already mentioned by the ancient Greeks, and recently studied by some authors. These two operations are algebraic and create new objects from given input objects. There is a surprisingly simple relation between the offset and the conchoid operation. As derived there exists a rational bijective quadratic map which transforms a given surface F and its offset surfaces Fd to a surface G and its conchoidal surface Gd, and vice versa. Geometric properties of this map are studied and illustrated at hand of some complete examples. Furthermore rational universal parameterizations for offsets and conchoid surfaces are provided

Offsets, Conchoids and Pedal Surfaces

We discuss three geometric constructions and their relations, namely the offset, the conchoid and the pedal construction. The offset surface F d of a given surface F is the set of points at fixed normal distance d of F. The conchoid surface G d of a given surface G is obtained by increasing the radius function by d with respect to a given reference point O. There is a nice relation between offsets and conchoids: The pedal surfaces of a family of offset surfaces are a family of conchoid surfaces. Since this relation is birational, a family of rational offset surfaces corresponds to a family of rational conchoid surfaces and vice versa. We present theoretical principles of this mapping and apply it to ruled surfaces and quadrics. Since these surfaces have rational offsets and conchoids, their pedal and inverse pedal surfaces are new classes of rational conchoid surfaces and rational offset surfaces

An integrated approach to high integrity software verification.

Computer software is developed through software engineering. At its most precise, software engineering involves mathematical rigour as formal methods. High integrity software is associated with safety critical and security critical applications, where failure would bring significant costs. The development of high integrity software is subject to stringent standards, prescribing best practises to increase quality. Typically, these standards will strongly encourage or enforce the application of formal methods. The application of formal methods can entail a significant amount of mathematical reasoning. Thus, the development of automated techniques is an active area of research. The trend is to deliver increased automation through two complementary approaches. Firstly, lightweight formal methods are adopted, sacrificing expressive power, breadth of coverage, or both in favour of tractability. Secondly, integrated solutions are sought, exploiting the strengths of different technologies to increase automation. The objective of this thesis is to support the production of high integrity software by automating an aspect of formal methods. To develop tractable techniques we focus on the niche activity of verifying exception freedom. To increase effectiveness, we integrate the complementary technologies of proof planning and program analysis. Our approach is investigated by enhancing the SPARK Approach, as developed by Altran Praxis Limited. Our approach is implemented and evaluated as the SPADEase system. The key contributions of the thesis are summarised below: • Configurable and Sound - Present a configurable and justifiably sound approach to software verification. • Cooperative Integration - Demonstrate that more targeted and effective automation can be achieved through the cooperative integration of distinct technologies. • Proof Discovery - Present proof plans that support the verification of exception freedom. • Invariant Discovery - Present invariant discovery heuristics that support the verification of exception freedom. • Implementation as SPADEase - Implement our approach as SPADEase. • Industrial Evaluation - Evaluate SPADEase against both textbook and industrial subprograms

Logic synthesis and optimisation using Reed-Muller expansions

This thesis presents techniques and algorithms which may be employed to represent, generate and optimise particular categories of Exclusive-OR SumOf-Products (ESOP) forms. The work documented herein concentrates on two types of Reed-Muller (RM) expressions, namely, Fixed Polarity Reed-Muller (FPRM) expansions and KROnecker (KRO) expansions (a category of mixed polarity RM expansions). Initially, the theory of switching functions is comprehensively reviewed. This includes descriptions of various types of RM expansion and ESOP forms. The structure of Binary Decision Diagrams (BDDs) and Reed-Muller Universal Logic Module (RM-ULM) networks are also examined. Heuristic algorithms for deriving optimal (sub-optimal) FPRM expansions of Boolean functions are described. These algorithms are improved forms of an existing tabular technique [1]. Results are presented which illustrate the performance of these new minimisation methods when evaluated against selected existing techniques. An algorithm which may be employed to generate FPRM expansions from incompletely specified Boolean functions is also described. This technique introduces a means of determining the optimum allocation of the Boolean 'don't care' terms so as to derive equivalent minimal FPRM expansions. The tabular technique [1] is extended to allow the representation of KRO expansions. This new method may be employed to generate KRO expansions from either an initial incompletely specified Boolean function or a KRO expansion of different polarity. Additionally, it may be necessary to derive KRO expressions from Boolean Sum-Of-Products (SOP) forms where the product terms are not minterms. A technique is described which forms KRO expansions from disjoint SOP forms without first expanding the SOP expressions to minterm forms. Reed-Muller Binary Decision Diagrams (RMBDDs) are introduced as a graphical means of representing FPRM expansions. RMBDDs are analogous to the BDDs used to represent Boolean functions. Rules are detailed which allow the efficient representation of the initial FPRM expansions and an algorithm is presented which may be employed to determine an optimum (sub-optimum) variable ordering for the RMBDDs. The implementation of RMBDDs as RM-ULM networks is also examined. This thesis is concluded with a review of the algorithms and techniques developed during this research project. The value of these methods are discussed and suggestions are made as to how improved results could have been obtained. Additionally, areas for future work are proposed

Digital Signal Processing (Second Edition)

This book provides an account of the mathematical background, computational methods and software engineering associated with digital signal processing. The aim has been to provide the reader with the mathematical methods required for signal analysis which are then used to develop models and algorithms for processing digital signals and finally to encourage the reader to design software solutions for Digital Signal Processing (DSP). In this way, the reader is invited to develop a small DSP library that can then be expanded further with a focus on his/her research interests and applications. There are of course many excellent books and software systems available on this subject area. However, in many of these publications, the relationship between the mathematical methods associated with signal analysis and the software available for processing data is not always clear. Either the publications concentrate on mathematical aspects that are not focused on practical programming solutions or elaborate on the software development of solutions in terms of working ‘black-boxes’ without covering the mathematical background and analysis associated with the design of these software solutions. Thus, this book has been written with the aim of giving the reader a technical overview of the mathematics and software associated with the ‘art’ of developing numerical algorithms and designing software solutions for DSP, all of which is built on firm mathematical foundations. For this reason, the work is, by necessity, rather lengthy and covers a wide range of subjects compounded in four principal parts. Part I provides the mathematical background for the analysis of signals, Part II considers the computational techniques (principally those associated with linear algebra and the linear eigenvalue problem) required for array processing and associated analysis (error analysis for example). Part III introduces the reader to the essential elements of software engineering using the C programming language, tailored to those features that are used for developing C functions or modules for building a DSP library. The material associated with parts I, II and III is then used to build up a DSP system by defining a number of ‘problems’ and then addressing the solutions in terms of presenting an appropriate mathematical model, undertaking the necessary analysis, developing an appropriate algorithm and then coding the solution in C. This material forms the basis for part IV of this work. In most chapters, a series of tutorial problems is given for the reader to attempt with answers provided in Appendix A. These problems include theoretical, computational and programming exercises. Part II of this work is relatively long and arguably contains too much material on the computational methods for linear algebra. However, this material and the complementary material on vector and matrix norms forms the computational basis for many methods of digital signal processing. Moreover, this important and widely researched subject area forms the foundations, not only of digital signal processing and control engineering for example, but also of numerical analysis in general. The material presented in this book is based on the lecture notes and supplementary material developed by the author for an advanced Masters course ‘Digital Signal Processing’ which was first established at Cranfield University, Bedford in 1990 and modified when the author moved to De Montfort University, Leicester in 1994. The programmes are still operating at these universities and the material has been used by some 700++ graduates since its establishment and development in the early 1990s. The material was enhanced and developed further when the author moved to the Department of Electronic and Electrical Engineering at Loughborough University in 2003 and now forms part of the Department’s post-graduate programmes in Communication Systems Engineering. The original Masters programme included a taught component covering a period of six months based on two semesters, each Semester being composed of four modules. The material in this work covers the first Semester and its four parts reflect the four modules delivered. The material delivered in the second Semester is published as a companion volume to this work entitled Digital Image Processing, Horwood Publishing, 2005 which covers the mathematical modelling of imaging systems and the techniques that have been developed to process and analyse the data such systems provide. Since the publication of the first edition of this work in 2003, a number of minor changes and some additions have been made. The material on programming and software engineering in Chapters 11 and 12 has been extended. This includes some additions and further solved and supplementary questions which are included throughout the text. Nevertheless, it is worth pointing out, that while every effort has been made by the author and publisher to provide a work that is error free, it is inevitable that typing errors and various ‘bugs’ will occur. If so, and in particular, if the reader starts to suffer from a lack of comprehension over certain aspects of the material (due to errors or otherwise) then he/she should not assume that there is something wrong with themselves, but with the author

Recognition and Exploitation of Gate Structure in SAT Solving

In der theoretischen Informatik ist das SAT-Problem der archetypische Vertreter der Klasse der NP-vollständigen Probleme, weshalb effizientes SAT-Solving im Allgemeinen als unmöglich angesehen wird. Dennoch erzielt man in der Praxis oft erstaunliche Resultate, wo einige Anwendungen Probleme mit Millionen von Variablen erzeugen, die von neueren SAT-Solvern in angemessener Zeit gelöst werden können. Der Erfolg von SAT-Solving in der Praxis ist auf aktuelle Implementierungen des Conflict Driven Clause-Learning (CDCL) Algorithmus zurückzuführen, dessen Leistungsfähigkeit weitgehend von den verwendeten Heuristiken abhängt, welche implizit die Struktur der in der industriellen Praxis erzeugten Instanzen ausnutzen. In dieser Arbeit stellen wir einen neuen generischen Algorithmus zur effizienten Erkennung der Gate-Struktur in CNF-Encodings von SAT Instanzen vor, und außerdem drei Ansätze, in denen wir diese Struktur explizit ausnutzen. Unsere Beiträge umfassen auch die Implementierung dieser Ansätze in unserem SAT-Solver Candy und die Entwicklung eines Werkzeugs für die verteilte Verwaltung von Benchmark-Instanzen und deren Attribute, der Global Benchmark Database (GBD)