The purpose of this research was to identify fission product signatures that could be used to uniquely identify a specific spent fuel assembly in order to improve international safeguards. This capability would help prevent and deter potential diversion of spent fuel for a nuclear weapons program. The power history experienced by a fuel assembly is distinct and could serve as the basis of a method for unique identification. Using fission product concentrations to characterize the assembly power history would limit the ability of a proliferator to deceive the identification method. As part of the work completed, the TransLat lattice physics code was successfully benchmarked for fuel depletion. By developing analytical models for potential monitor isotopes an understanding was built of how specific isotope characteristics affect the production and destruction mechanisms that determine fission product concentration. With this knowledge potential monitor isotopes were selected and tested for concentration differences as a result of power history variations. Signature ratios were found to have significant concentration differences as a result of power history variations while maintaining a constant final burnup. A conceptual method for implementation of a fission product identification system was proposed in conclusion.

The purpose of this research was to identify fission product signatures that could be used to uniquely identify a specific spent fuel assembly in order to improve international safeguards. This capability would help prevent and deter potential diversion of spent fuel for a nuclear weapons program. The power history experienced by a fuel assembly is distinct and could serve as the basis of a method for unique identification. Using fission product concentrations to characterize the assembly power history would limit the ability of a proliferator to deceive the identification method. As part of the work completed, the TransLat lattice physics code was successfully benchmarked for fuel depletion. By developing analytical models for potential monitor isotopes an understanding was built of how specific isotope characteristics affect the production and destruction mechanisms that determine fission product concentration. With this knowledge potential monitor isotopes were selected and tested for concentration differences as a result of power history variations. Signature ratios were found to have significant concentration differences as a result of power history variations while maintaining a constant final burnup. A conceptual method for implementation of a fission product identification system was proposed in conclusion.

The objectives of this study were to identify signatures of selection in Bos taurus beef and dairy cattle populations and to annotate regions of selection with gene, function and QTL information. Differences in minor allele frequencies, population-average FST, population-specific FST, and integrated extended haplotype homozygosity scores were applied to a subset of the bovine HapMap data to characterize signatures of selection in 7 Bos taurus beef and 5 Bos taurus dairy cattle populations. Numerous single nucleotide polymorphisms (SNP) exhibited evidence of selection across the genome and regions of BTA2 and BTA14 that are considered to be under positive selection in beef and dairy cattle, respectively, were highlighted. The current density of SNP limited our ability to annotate regions putatively under selection because most SNP in the assay were intergenic. This is likely because of the betweenbreed SNP discovery method that was used, which typically identifies SNP with higher allele frequencies.

Thesis (Ph.D.) - James Cook University, 2003. / Typescript (photocopy) Bibliography: leaves 181-196.

Th.--Sci.--Paris 12, 1979.

Within the last 100 years, commercial chickens have been split into two main groups. Broiler chickens are produced for meat production while layers are produced for egg production. This has caused large phenotypic changes and the genomic signatures of selection may be detectable using statistical techniques. Genomic regions identified by these techniques may include genes associated with production traits, and is therefore of interest to animal breeders. This thesis investigates signatures of selection in a number of commercial chicken lines using several statistical techniques based on population differentiation and levels of genetic diversity. First, signatures of selection were investigated using population differentiation in nine lines of broiler chickens. Weir and Cockerham's pairwise FST was calculated for genome-wide markers between the broiler lines and averaged into overlapping sliding windows to remove stochastic effects. A chromosome bound, circular permutation method was used to generate a null distribution and determine the significance of each window. A total of 51 putative selection signatures were found shared between lines and 87 putative selection signatures were found to be unique to one line. The majority of these regions contain peak positions for broiler QTL found in previous studies and eight regions were significantly enriched for broiler QTL. One region located on chromosome 27 contained 39 broiler QTL and 114 genes, several of which were functional candidates for association with broiler traits. Secondly, areas of low diversity were investigated in three different SNP datasets. All three datasets were taken from the same broiler line at different time points and consisted of different SNP densities, including 12k, 42k and 600k. A number of zero diversity regions were found in each dataset and several were shared between the datasets. The 600k dataset was also analysed using a regression test, which investigates the patterns of diversity as the distance from the selected site increases. This method searches for signatures of selections by fitting a regression to the diversity data to test the fit of the data to the theoretical model. A total of 15 regions were found displaying significant asymptotic regression and diversity values less than 0.005. One of these regions located on chromosome 1 was also found as a fixed region in the 12k and 42k datasets and contained the gene IGF1, which encodes an important protein for growth. Finally, signatures of selection were investigated between broiler and layer datasets by investigating population differentiation and diversity based analysis. Weir and Cockerham's pairwise FST was calculated between the two lines and outliers extracted. A total of 32 regions were found displaying high differentiation. Seven regions of low diversity in the layer dataset were also investigated. Several broiler and layer QTL had been previously identified in these regions. Two genes related to hedgehog proteins were identified within selected regions, which are known to be involved in embryogenesis. Finally seven regions were found to be highly differentiated between the broiler and layer lines, and the nine broiler lines in the first chapter. This may indicate selection which occurred during breed separation. Signatures of selection were identified in four broiler and layer datasets using several statistical techniques. A number of regions were identified in multiple datasets by a number of techniques and are therefore good candidate regions for selection. Other statistical techniques could be used in future studies to further confirm these regions and identify causative genes and variants.

No description available.

Thesis (PhD)--Stellenbosch University, 2015 / AFRIKAANSE OPSOMMING : In hierdie verhandeling stel ons 'n nuwe strategie vir outomatiese handtekening-verifikasie voor. Die voorgestelde raamwerk gebruik 'n skrywer-onafhanklike benadering tot handtekening- modellering en is dus in staat om bevraagtekende handtekeninge, wat aan enige skrywer behoort, te bekragtig, op voorwaarde dat minstens een outentieke voorbeeld vir vergelykingsdoeleindes beskikbaar is. Ons ondersoek die tradisionele statiese geval (waarin 'n bestaande pen-op-papier handtekening vanuit 'n versyferde dokument onttrek word), asook die toenemend gewilde dinamiese geval (waarin handtekeningdata outomaties tydens ondertekening m.b.v. gespesialiseerde elektroniese hardeware bekom word). Die statiese kenmerk-onttrekkingstegniek behels die berekening van verskeie diskrete Radontransform (DRT) projeksies, terwyl dinamiese handtekeninge deur verskeie ruimtelike en temporele funksie-kenmerke in die kenmerkruimte voorgestel word. Ten einde skryweronafhanklike handtekening-ontleding te bewerkstellig, word hierdie kenmerkstelle na 'n verskil-gebaseerde voorstelling d.m.v. 'n geskikte digotomie-transformasie omgeskakel. Die klassikasietegnieke, wat vir handtekeking-modellering en -verifikasie gebruik word, sluit kwadratiese diskriminant-analise (KDA) en steunvektormasjiene (SVMe) in. Die hoofbydraes van hierdie studie sluit twee nuwe tegnieke, wat op die bou van 'n robuuste skrywer-onafhanklike handtekeningmodel gerig is, in. Die eerste, 'n dinamiese tydsverbuiging digotomie-transformasie vir statiese handtekening-voorstelling, is in staat om vir redelike intra-klas variasie te kompenseer, deur die DRT-projeksies voor vergelyking nie-lineêr te belyn. Die tweede, 'n skrywer-spesieke verskil-normaliseringstrategie, is in staat om inter-klas skeibaarheid in die verskilruimte te verbeter deur slegs streng relevante statistieke tydens die normalisering van verskil-vektore te beskou. Die normaliseringstrategie is generies van aard in die sin dat dit ewe veel van toepassing op beide statiese en dinamiese handtekening-modelkonstruksie is. Die stelsels wat in hierdie studie ontwikkel is, is spesi ek op die opsporing van hoë-kwaliteit vervalsings gerig. Stelselvaardigheid-afskatting word met behulp van 'n omvattende eksperimentele protokol bewerkstellig. Verskeie groot handtekening-datastelle is oorweeg. In beide die statiese en dinamiese gevalle vaar die voorgestelde SVM-gebaseerde stelsel beter as die voorgestelde KDA-gebaseerde stelsel. Ons toon ook aan dat die stelsels wat in hierdie studie ontwikkel is, die meeste bestaande stelsels wat op dieselfde datastelle ge evalueer is, oortref. Dit is selfs meer belangrik om daarop te let dat, wanneer hierdie stelsels met bestaande tegnieke in die literatuur vergelyk word, ons aantoon dat die gebruik van die nuwe tegnieke, soos in hierdie studie voorgestel, konsekwent tot 'n statisties beduidende verbetering in stelselvaardigheid lei. / ENGLISH ABSTRACT : In this dissertation we present a novel strategy for automatic handwritten signature verification. The proposed framework employs a writer-independent approach to signature modelling and is therefore capable of authenticating questioned signatures claimed to belong to any writer, provided that at least one authentic sample of said writer's signature is available for comparison. We investigate both the traditional off-line scenario (where an existing pen-on-paper signature is extracted from a digitised document) as well as the increasingly popular on-line scenario (where the signature data are automatically recorded during the signing event by means of specialised electronic hardware). The utilised off-line feature extraction technique involves the calculation of several discrete Radon transform (DRT) based projections, whilst on-line signatures are represented in feature space by several spatial and temporal function features. In order to facilitate writer-independent signature analysis, these feature sets are subsequently converted into a dissimilarity-based representation by means of a suitable dichotomy transformation. The classification techniques utilised for signature modelling and verification include quadratic discriminant analysis (QDA) and support vector machines (SVMs). The major contributions of this study include two novel techniques aimed towards the construction of a robust writer-independent signature model. The first, a dynamic time warping (DTW) based dichotomy transformation for off-line signature representation, is able to compensate for reasonable intra-class variability by non-linearly aligning DRT-based projections prior to matching. The second, a writer-specific dissimilarity normalisation strategy, improves inter-class separability in dissimilarity space by considering only strictly relevant dissimilarity statistics when normalising the dissimilarity vectors belonging to a specific individual. This normalisation strategy is generic in the sense that it is equally applicable to both off-line and on-line signature model construction. The systems developed in this study are specifically aimed towards skilled forgery detection. System proficiency estimation is conducted using a rigorous experimental protocol. Several large signature corpora are considered. In both the off-line and on-line scenarios, the proposed SVM-based system outperforms the proposed QDA-based system. We also show that the systems proposed in this study outperform most existing systems that were evaluated on the same data sets. More importantly, when compared to state-of-the-art techniques currently employed in the literature, we show that the incorporation of the novel techniques proposed in this study consistently results in a statistically significant improvement in system proficiency.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Demand of automobiles are significantly growing despite various factors, steadily increasing the average number of vehicles on the road. Increase in the number of vehicles, subsequently increases the risk of collisions, characterized by the driving behavior. Driving behavior is influenced by factors like class of vehicle, road condition and vehicle maneuvering by the driver. Rapidly growing mobile technology and use of smartphones embedded with in-built sensors, provides scope of constant development of assistance systems considering the safety of the driver by integrating with the information obtained from the vehicle on-board sensors. Our research aims at learning a vehicle system comprising of vehicle, human and road by employing driving patterns obtained from the sensor data to develop better systems of safety and alerts altogether. The thesis focusses on utilizing together various data recorded by the in-built embedded sensors in a smartphone to understand the vehicle motion and dynamics, followed by studying various impacts of collision events, types and signatures which can potentially be integrated in a prototype framework to detect variations, alert drivers and emergency responders in an event of collision.