Backward conditioning: A new program specialisation technique and its application to program comprehension

This paper introduces backward conditioning. Like forward conditioning (used in conditioned slicing), backward conditioning consists of specialising a program with respect to a condition inserted into the program. However, whereas forward conditioning deletes statements which are not executed when the initial state satisfies the condition, backward conditioning deletes statements which cannot cause execution to enter a state which satisfies the condition. The relationship between backward and forward conditioning is reminiscent of the relationship between backward and forward slicing. Forward conditioning addresses program comprehension questions of the form `what happens if the program starts in a state satisfying condition c?`, whereas backward conditioning addresses questions of the form `what parts of the program could potentially lead to the program arriving in a state satisfying condition c?' The paper illustrates the use of backward conditioning as a program comprehension assistant and presents an algorithm for constructing backward conditioned programs

Distributed collaboration between industry and university partners in HE

Over the past three years the School of Design has been experimenting with an innovative curriculum design and delivery model named ‘the Global Studio’. The Global Studio is a cross-institutional research informed teaching and learning collaboration conducted between Northumbria University and international universities and industry partners based in the UK, USA, Netherlands and Korea. The aims of the Global Studio are directly linked with current and future industry needs that are related to changes in the organisation of product and service development. These changes highlight the importance of equipping design students with skills for working in globally networked organisations particularly the development of skills in intercultural communication and collaboration. In this paper we will focus on the Global Studio conducted in 2008 which included Northumbria University (UK), Hongik University (Korea), Auburn University (USA), Intel (USA), Motorola design studios located in the UK and Korea and Great Southern Wood (USA). These projects will be used to illustrate challenges and benefits of international collaborative industry-based projects undertaken in distributed settings

A Denotational Interprocedural Program Slicer

This paper extends a previously developed intraprocedural denotational program slicer to handle procedures. Using the denotational approach, slices can be defined in terms of the abstract syntax of the object language without the need of a control flow graph or similar intermediate structure. The algorithm presented here is capable of correctly handling the interplay between function and procedure calls, side-effects, and short-circuit expression evaluation. The ability to deal with these features is required in reverse engineering of legacy systems, where code often contains side-effects

Evolutionary testing supported by slicing and transformation

Evolutionary testing is a search based approach to the automated generation of systematic test data, in which the search is guided by the test data adequacy criterion. Two problems for evolutionary testing are the large size of the search space and structural impediments in the implementation of the program which inhibit the formulation of a suitable fitness function to guide the search. In this paper we claim that slicing can be used to narrow the search space and transformation can be applied to the problem of structural impediments. The paper presents examples of how these two techniques have been successfully employed to make evolutionary testing both more efficient and more effective

Determination of suitable housekeeping genes for normalisation of quantitative real time PCR analysis of cells infected with human immunodeficiency virus and herpes viruses

The choice of an appropriate housekeeping gene for normalisation purposes has now become an essential requirement when designing QPCR experiments. This is of particular importance when using QPCR to measure viral and cellular gene transcription levels in the context of viral infections as viruses can significantly interfere with host cell pathways, the components of which traditional housekeeping genes often encode. In this study we have determined the reliability of 10 housekeeping genes in context of four heavily studied viral infections; human immunodeficiency virus type 1, herpes simplex virus type 1, cytomegalovirus and varicella zoster virus infections using a variety of cell types and virus strains. This provides researchers of these viruses with a shortlist of potential housekeeping genes to use as normalisers for QPCR experiments

A Review of Automatic Classification of Drones Using Radar:Key Considerations, Performance Evaluation and Prospects

Automatic target classification or recognition is a critical capability in non-cooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognising targets, including miniature unmanned air systems or drones (i.e., small, mini, micro and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar

Advanced cognitive networked radar surveillance

The concept of a traditional monostatic radar with co-located transmit and receive antennas naturally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications. Initial experimental results are presented for a 2-node networked staring radar