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2009 MRes projects viewer

An evaluation of CCTV monitoring strategies for hospital security

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Small vehicle inspection scanner imaging: SVXi

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Attention to detail predicts threat detection performance in security X-ray images

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Predicting the position of the source of blood stains for angled impacts on fabrics and exploring the effects of surface roughness on stain characteristics

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Hester Miles

The extent to which two-dimensional stain characteristics of bloodstains generated at angled impacts can be analysed to infer the precise three-dimensional drop origin characteristics was examined. Proxy blood drops were passively dropped from 4 heights (50cm, 100cm, 150cm, 200cm) to impact onto two different clothing items at 6 different impact angles (22.7o, 43.5o, 56.3o, 61.6o, 78.8o, 90o) (Knock. Davison. 2007).
Clothing differed in terms of fabric composition and surface roughness. Stains generated were then analysed to obtain measurements of main stain width, length and number of satellite stains generated. Relationships were observed between drop height and stain characteristics, and impact angle and stain characteristics.

Use of a mirror-symmetry prior in small vehicle imaging

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Theo Mourouzis

Symmetry is a fundamental concept which can be used as a complexity-reducing tool that we find in many natural and man-made objects. The three types of symmetry; mirror, rotational and translational are directly related to shape perception. Formally, each type is defined as an invariant under some Euclidean transformation, such as reflection, rotation and translation, respectively.
The main aim of this project is to examine if the mirror-symmetry prior can be used successfully in small vehicle imaging for the detection of any suspicious items that are presented. As the initial problem involves a lot of complexities, we employ a simplified version of the problem, where the imaged subject  (P) is decomposed to (S)+(N), subject to different constraints, where (N) is a mirror-symmetric part and (S) is the part arising from the trivial subtraction (P)-(S) . The decomposition is done using the Simulated Annealing Algorithm (SAA) a heuristic probabilistic algorithm. Initially this idea was employed for the 3D reconstruction of a given structure from some 2D projection images, but then due to some computational difficulties we decided to limit our study to these specific 2D projection images.

Beyond primary transfer: The secondary transfer of geoforensic trace particulates and their dissemination within social networks

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James French

Transfer experiments were undertaken in order to explore the topic of the secondary transfer of trace geoforensic particulates and the potential for evidence dissemination within social/contact networks.  UV-powder was introduced to an individual in three experimental scenarios.  Samples from individuals and surfaces were analysed using an image-rasterisation technique in MATLAB which quantified the recovered material.

Trace geoforensic particulates underwent secondary, tertiary and quaternary transfers along chains of individuals and surfaces, while particulates were disseminated within the entire contact network of an individual.  Such findings can inform forensic protocol: Sampling the entirety of contact networks is recommended, while amalgamating transfers of different orders during analysis could curtail the sequencing of transfers.  The possibility of secondary transfer to innocent parties must be heeded when interpreting evidence, as well as the possibility of mistaking recent secondary transfers for decayed primary transfers. 

Assessing and improving whole body scanners through public involvement

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Can crime science tools help tackle internal child sex trafficking in the UK?

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Speech enhancement using the binary mask method and its application to law enforcement

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Toby Davies

This work presents an analysis of the recently-proposed 'binary mask' technique for the enhancement of intelligibility of speech in noise, with a view to its deployment as a forensic tool within law enforcement, an area for which machine-learning algorithms are feasible. Computational analysis of the algorithm and its variants is carried out with a view to increasing understanding an optimisation of key parameters, and this is followed by intelligibility testing with human listeners.
In both cases the performance of previous work is not achieved, with significant deterioration of intelligibility instead being shown. Other factors, however, such as alternative mask definition and use of data smoothing, are seen to have intriguing effects on both pattern classification performance and intelligibility. Analysis of the technique from a legal perspective is also provided, focussing on the potential admissibility of such a technique in court and the requirements for legal validation as a forensic tool.

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